Bachelor Informatik

Fast facts

Department
Informatik
Stand/version
2026
Standard period of study (semester)
6
ECTS
0

Study plan

1. Semester	2. Semester	3. Semester	4. Semester	5. Semester	6. Semester
Algorithmen und Programmierung PF 5SWS 5ECTS	Mathematik für Informatik 2 PF 4SWS 5ECTS	Datenvisualisierung PF 4SWS 5ECTS	Data Science Datenbanken PF 4SWS 5ECTS	ERP 2 PF 4SWS 5ECTS	Thesis mit Kolloquium PF 4SWS 15ECTS
Algorithmen und Programmierung – Projektwoche PF 2SWS 2.5ECTS	Technisches Englisch PF 2SWS 2.5ECTS	IT-Recht PF 2SWS 2.5ECTS	Grundlagen des Maschinellen Lernens PF 4SWS 5ECTS	Data Science Projekt PF 4SWS 5ECTS
BWL PF 4SWS 5ECTS	Datenbanken 1 PF 4SWS 5ECTS	Mathematik für Informatik 4 Data Science PF 4SWS 5ECTS	Informationssicherheit PF 4SWS 5ECTS	Informatik und Gesellschaft PF 2SWS 2.5ECTS
Mathematik für Informatik 1 PF 4SWS 5ECTS	Lern- u. Arbeitstechniken PF 2SWS 2.5ECTS	Mensch Computer Interaktion PF 4SWS 5ECTS	Kommunikations- und Rechnernetze PF 4SWS 5ECTS	Projektarbeit PF 0SWS 5ECTS
Rechnerstrukturen und Betriebssysteme 1 PF 4SWS 5ECTS	Mathematik für Informatik 3 PF 4SWS 5ECTS	Programmierkurs Anwendungsentwicklung PF 4SWS 5ECTS	Künstliche Intelligenz PF 4SWS 5ECTS	Seminar (Inhalt) PF 2SWS 2.5ECTS
Theoretische Informatik PF 4SWS 5ECTS	Objektorientierte Programmierung und Datenstrukturen PF 5SWS 5ECTS	Programmierkurs Python und R PF 4SWS 5ECTS	Softwaretechnik 2 PF 4SWS 5ECTS	Compulsory elective modules (26)
Compulsory elective modules (2)	Objektorientierte Programmierung und Datenstrukturen – Projektwoche PF 2SWS 2.5ECTS	Seminar (Methodik) PF 2SWS 2.5ECTS	Studium Generale PF 2SWS 2.5ECTS
	Rechnerstrukturen und Betriebssysteme 2 PF 4SWS 5ECTS	Softwaretechnik 1 PF 4SWS 5ECTS	Web-Technologien PF 4SWS 5ECTS
	Studium Generale PF 2SWS 2.5ECTS

Compulsory elective modules 1. Semester

Ausgewählte Aspekte der Data Science 1

WP
4SWS
5ECTS

Ausgewählte Aspekte der Data Science 2

WP
4SWS
5ECTS

Compulsory elective modules 2. Semester

Compulsory elective modules 3. Semester

Compulsory elective modules 4. Semester

Compulsory elective modules 5. Semester

Anerkannte Wahlpflichtprüfungsleistung

WP
4SWS
5ECTS

Anerkannte Wahlpflichtprüfungsleistung

WP
0SWS
5ECTS

Anerkannte Wahlpflichtprüfungsleistung

WP
0SWS
5ECTS

Ausgewählte Aspekte der Data Science 3

WP
4SWS
5ECTS

Ausgewählte Aspekte der Informatik 1 (Katalog Informatik gültig für Data Science)

WP
4SWS
5ECTS

Ausgewählte Aspekte der Informatik 2 (Katalog Informatik gültig für Data Science)

WP
4SWS
5ECTS

Ausgewählte Aspekte der Informatik 3 (Katalog Informatik gültig für Data Science)

WP
4SWS
5ECTS

Effiziente Algorithmen und Datenstrukturen

WP
4SWS
5ECTS

Fortgeschrittene Informationssicherheit

WP
4SWS
5ECTS

Informations- und Business Performance Management

WP
4SWS
5ECTS

Informationssysteme im Gesundheitswesen

WP
4SWS
5ECTS

Softwaretechnik C (Softwaremanagement)

WP
4SWS
5ECTS

Softwaretechnik D (Qualitätssicherung und Wartung)

WP
4SWS
5ECTS

Virtualisierung und Cloud Computing

WP
4SWS
5ECTS

Compulsory elective modules 6. Semester

Module overview

1. Semester of study

Algorithmen und Programmierung
PF

5 SWS

5 ECTS

Number
41011
Language(s)
de
Duration (semester)
1
Contact time
75 h
Self-study
75 h

Learning outcomes/competences

Learning outcomes / competences
After successfully completing this module, students will be able to:
Knowledge and understanding:

Explain principles, methods, concepts and notations of "programming in miniature"
Explain basic elements of imperative programs such as data types and operators, expressions and methods, control structures and fields.

read programs written in a given programming language and understand and predict their execution
use the principles, concepts, notations and basic elements learned in programs.
apply rules for the formation of expressions using examples.
understand problem descriptions and construct example inputs and outputs for the problem
to independently design a solution to a given problem in the form of an algorithm.
create a program in a development environment step by step, test it, and detect and correct errors.
program sorting algorithms and compare their runtime complexity

understand and describe a problem solution with the help of examples
formulate a solution to a problem and in the form of an imperative algorithm
develop small programs in teams to solve small problems and communicate about errors and possible solutions.

Analyze the quality of programs

Computers, computer science
Java introduction
Data types and operators
Expressions, methods
Control structures
Algorithms
Fields
Recursion
Complexity
Search
Sort

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection
Solving practical exercises in individual or team work
Processing programming tasks on the computer in individual or team work
Active, self-directed learning through internet-supported tasks, sample solutions and accompanying materials

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written written examination
study achievements during the semester (bonus points)

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor of Business Informatics
Bachelor of Software and Systems Engineering (dual)
Bachelor of Computer Science
Bachelor's degree in Medical Informatics
Bachelor of Medical Informatics Dual
Bachelor of Computer Science Dual

Literature

D. Ratz, D. Schulmeister-Zimolong, D. Seese, J. Wiesenberger, Grundkurs Programmieren in Java, 9. Auflage, Hanser, 2024
C. Ullenboom, Java ist auch eine Insel, 17. Auflage, Galileo Press, 2023
A. Solymosi, U. Grude, Grundkurs Algorithmen und Datenstrukturen in JAVA, Springer Vieweg 2017
R. Sedgewick, K. Wayne, Algorithmen: Algorithmen und Datenstrukturen, 4. Auflage, Pearson Studium 2014
H. Balzert, Java: Objektorientiert programmieren, 3. Auflage, Springer Campus, 2017

Algorithmen und Programmierung – Projektwoche
PF

2 SWS

2.5 ECTS

Number
41012
Language(s)
de
Duration (semester)
1
Contact time
30h
Self-study
45h

Learning outcomes/competences

Knowledge and understanding

Students understand basic concepts of console programming and algorithmic problem solving.

Use, application and generation of knowledge

Students can plan, develop and implement functional console applications independently and in a team under time pressure.

Communication and cooperation

Students can plan and implement software projects in small teams and explain their own code and team solutions in an understandable way.

Scientific self-image / professionalism

Students reflect on their own working methods under time pressure, take responsibility for individual tasks in the team and develop a professional understanding of software development.

The project week is held as a five-day block course following the lecture "Algorithms and Programming" and includes the development of console applications for given tasks in individual and team work. In-depth knowledge of the contents of "Algorithms and Programming" is assumed.

Teaching methods

Processing programming tasks on the computer in individual and team work.

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of an ungraded practical examination on the computer, in which students demonstrate that they are able to plan a console application to solve given problems under time pressure and implement it in a given programming language.
Duration: 180 minutes.

Requirements for the awarding of credit points

In order to enable teamwork and to be able to accompany the professional creation of the programs by the teachers, a minimum attendance requirement with active participation of 80% is required.

A prerequisite for participation in the practical examination is proof of active participation in the current or a previous project week.

The module examination consists of a 180-minute ungraded practical examination, which must be passed.

If the minimum attendance requirement is not met, there is no entitlement to participate in the module examination. If registration is nevertheless made and not withdrawn within the deadline, the examination is deemed to have been "failed".

Applicability of the module (in other degree programs)

Bachelor of Computer Science
Bachelor's degree in Medical Informatics
Bachelor of Medical Informatics Dual

Literature

D. Ratz, D. Schulmeister-Zimolong, D. Seese, J. Wiesenberger, Grundkurs Programmieren in Java, 9. Auflage, Hanser, 2024
C. Ullenboom, Java ist auch eine Insel, 17. Auflage, Galileo Press, 2023
A. Solymosi, U. Grude, Grundkurs Algorithmen und Datenstrukturen in JAVA, Springer Vieweg 2017
R. Sedgewick, K. Wayne, Algorithmen: Algorithmen und Datenstrukturen, 4. Auflage, Pearson Studium 2014
H. Balzert, Java: Objektorientiert programmieren, 3. Auflage, Springer Campus, 2017

BWL
PF

4 SWS

5 ECTS

Number
45281
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Knowledge and understanding:

Students understand the basic concepts of business administration and their relevance to the field of IT.
They know the differences between cost centers, cost types and cost units.

Use, application and generation of knowledge:

Students analyze the business and legal consequences of operational decisions
They are proficient in cost accounting methods, in particular cost types, cost centers and cost unit accounting.
You will use costing techniques to evaluate projects and investments from a business studies perspective.
They understand materials management, warehousing, production management and sales management and can optimize operational processes.

Communication and cooperation:

The students work in groups on business management tasks and learn about the requirements of team processes.
They present business studies and discuss operational decision-making processes.

Scientific self-image / professionalism:

Students reflect on business management decisions and their impact on companies
They are able to critically scrutinize Business Studies concepts and make sound business decisions.

Historical development of Business Studies
Legal foundations
Operation and company, structure, organization and task of company divisions
Procurement management
Materials and warehouse management
Production management
Sales management
Business accounting, calculations and cost accounting, BAB
ABC analysis and project management (network planning technique)
Company formation, types of company, capital increase

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection
Solving practical exercises in individual or team work
Group work
Individual work
Active, self-directed learning through internet-supported tasks, sample solutions and accompanying materials

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)
Bachelor of Computer Science
Bachelor's degree in Medical Informatics
Bachelor of Medical Informatics Dual
Bachelor of Computer Science Dual

Literature

Philip Junge: BWL für Ingenieure, Springer Verlag 2012
Kruse/Heun : Betriebswirtschaftslehr, Winklers Verlag
Deitermann, M., Schmolke, S., IKR mit Kosten- und Leistungsrechnung, Winklers Verlag

Mathematik für Informatik 1
PF

4 SWS

5 ECTS

Number
41064
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students will be able to

apply the basic concepts of set theory, understand the associated formula language and apply it independently,
apply the concept of a relation between elements of one or two different sets,
apply algebraic concepts such as the notions of an (abelian) group, a solid and a vector space and give concrete examples of finite and infinite groups and solids,
calculate with complex numbers and use their different types of representation to solve problems,
apply the mathematical techniques of matrix and vector arithmetic,
examine any system of linear equations with regard to their solvability and the structure of their solution set and name their relationship with properties of the associated coefficient matrix (determinate, rank, kernel and image)
determine the solution set of any system of linear equations using suitable mathematical methods,
answer geometric questions about positional relationships, distances and angles in two- or three-dimensional Euclidean space using linear algebra techniques

The event includes the following topics:

Basics of mathematics for computer scientists: Introduction to set theory, cardinality of sets, relations, basics of propositional logic, complex numbers, groups and solids.
Vectors and vector calculus: notation and interpretation, operations on vectors and their properties (addition, scalar multiplication, scalar product, cross product), vector spaces, length of vectors, collinearity, linear dependence and independence, concepts of dimension and basis, angles between vectors.
Lines and planes: Representation in linear algebra, applications, positional relationships between points / straight line / planes
Matrices: Notation and interpretation, operations on matrices and their properties (transposing matrices, addition, scalar multiplication, matrix multiplication), Gaussian algorithm, determinants, inverse matrices and their calculation
Linear systems of equations: motivation and applications, matrix-vector form of linear systems of equations, Gaussian algorithm for solving linear systems of equations, homogeneous and inhomogeneous linear systems of equations and their relationships, rank of a matrix and relation to the solution set of linear systems of equations
Eigenvalues

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection
Solving practical exercises in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written written examination lasting 90 - 120 minutes. The actual duration will be announced in the examination schedule. By completing the written examination tasks, students demonstrate the degree to which they have achieved the learning objectives by recalling the specialist knowledge taught, applying calculation methods and using them to answer in-depth questions.

Requirements for the awarding of credit points

Passed written exam

Applicability of the module (in other degree programs)

Bachelor of Computer Science
Bachelor's degree in Medical Informatics
Bachelor of Medical Informatics Dual
Bachelor of Computer Science Dual

Literature

Skript zur Vorlesung,
G. Teschl und S. Teschl, Mathematik für Informatiker 1, 3. Auflage, Springer Verlag (2008) - im Intranet der FH elektronisch verfügbar.
G. Teschl und S. Teschl, Mathematik für Informatiker 2, 2. Auflage, Springer Verlag (2007) - im Intranet der FH elektronisch verfügbar.
G. Fischer, Lineare Algebra, Vieweg, Braunschweig/Wiesbaden, 12. Auflage (2000).
Preuß, W., Wenisch, G., Lehr- und Übungsbuch Mathematik für Informatiker.

Rechnerstrukturen und Betriebssysteme 1
PF

4 SWS

5 ECTS

Number
41031
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful completion of the module, students will be able to:

Knowledge and understanding

explain the basic concepts of computer structures and operating systems, including number and character representation, digital technology, computer architecture and operating system functions.

Use, application and generation of knowledge

analyze digital circuits using Boolean algebra and design simple circuit networks and switching systems
interpret basic machine programs and understand their effects on hardware
apply Linux operating systems practically, especially in dealing with file systems and processes

Communication and cooperation

work on programming and analysis tasks in groups of two and present results in a structured manner
communicate technical contexts from the areas of computer structures and operating systems in an understandable way.

Scientific self-image / professionalism

Critically reflect on concepts of digital technology, computer architecture and operating systems in a technical and social context
to independently acquire further knowledge in the field of computer architectures and operating systems

Number and character representation (positive and negative integers, ASCII/Unicode)
Basics of digital technology (switching algebra, gates, normal forms, optimizations)
Arithmetic and logic (simple standard switching networks - from multiplexer to ALU)
Memory (RS latch, reference to automata theory, flip-flops, simple standard switching networks)
Computer architecture (machine types, von Neumann and Harvard, approaches to modernization, current processors)
Microprocessor architecture and programming (case study Microchip AVR ATmega)
Introduction to the practical application of Linux (file and directory management, input/output redirection, processes)
Operating system concepts (architectures)
Processes (administration, scheduling)
Memory management (free memory management, swapping, virtual memory)
File systems (FAT, Unix inodes)

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection
Exercise accompanying the lecture
Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written examination paper [scope: 100%] (90min); examinations during the semester (bonus points)

Requirements for the awarding of credit points

Passing a 90-minute graded written exam with at least sufficient (4.0)

Applicability of the module (in other degree programs)

Bachelor of Computer Science
Bachelor of Computer Science Dual

Literature

Tanenbaum, A.S., Rechnerarchitektur: Von der digitalen Logik zum Prarallelrechner, 6. Aufl., Pearson Studium, 2014.
Hoffmann, D.W., Grundlagen der Technischen Informatik, 7. Aufl., Hanser, 2023.
Tanenbaum, A.S., Moderne Betriebssysteme, 4. Aufl., Pearson Studium, 2016.
Stallings, W., Operating Systems: Internals and Design Principles, 9th ed., Prentice Hall, 2017.

Theoretische Informatik
PF

4 SWS

5 ECTS

Number
42041
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Technical and methodological competence:

Be able to name basic terms and properties of formal languages, grammars and the corresponding automata
Create grammars and automata for formal languages and understand how they work.
Be able to convert the representation of languages between grammars, automata and regular expressions.

Interdisciplinary methodological competence:

Be able to independently assess and classify problems in terms of their complexity

Formal languages and grammars: Alphabet; words: languages; grammars; derivations; grammar types in the Chomsky hierarchy
Regular languages: programming finite automata (deterministic and non-deterministic); minimization of automata; regular expressions; conversion between grammars, automata and regular expressions; closure properties, pumping lemma for regular languages
Context-free languages: pushdown automata; Chomsky normal form; word problem with the CYK algorithm; termination properties; pumping lemma for context-free languages
Turing machines: variants (deterministic and non-deterministic); universal Turing machines; Gödel number; P/NP problem

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection
Exercise accompanying the lecture
Solving practical exercises in individual or team work
Group work
Individual work
Presentation
Mini-exams during the semester for regular feedback

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written written examination
study achievements during the semester (bonus points)

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)
Bachelor of Computer Science
Bachelor's degree in Medical Informatics
Bachelor of Computer Science Dual

Literature

Hopcroft, J.E., Motwani, R., Ullman, J.D.; Einführung in die Automatentheorie, Formale Sprachen und Berechenbarkeit; Pearson Studium; 3. Auflage; 2011
Hoffmann, D.W.; Theoretische Informatik; Hanser; 5. Auflage; 2022
Hedtstück, U.: Einführung in die Theoretische Informatik; Oldenbourg; 5. Auflage; 2012
Erk, K., Priese, L.; Theoretische Informatik; Springer; 4. Auflage; 2018

Ausgewählte Aspekte der Data Science 1
WP

4 SWS

5 ECTS

Number
46113
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

This module combines courses on various data science topics that are not offered regularly. The contents and competencies are published each semester in an additional document.
The competencies are derived from the published supplementary document for the specific course.

The contents can be found in the published supplementary document for the specific event.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Lecture in seminar style, with blackboard and projection

exercise accompanying the lecture

Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written written examination

project-related work with documentation and presentation followed by an oral examination

oral examinations

Homework

Presentations

Requirements for the awarding of credit points

passed exam

Applicability of the module (in other degree programs)

Master's degree in Computer Science

Literature

siehe Zusatzdokument zur konkreten Veranstaltung

Ausgewählte Aspekte der Data Science 2
WP

4 SWS

5 ECTS

Number
46114
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

The contents can be found in the published supplementary document for the specific event.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Lecture in seminar style, with blackboard and projection

exercise accompanying the lecture

Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written written examination

project-related work with documentation and presentation followed by an oral examination

oral examinations

Homework

Presentations

Requirements for the awarding of credit points

passed exam

Applicability of the module (in other degree programs)

Master's degree in Computer Science

Literature

siehe Zusatzdokument zur konkreten Veranstaltung

2. Semester of study

Mathematik für Informatik 2
PF

4 SWS

5 ECTS

Number
41061
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Knowledge and understanding

Students understand basic concepts of higher mathematics, in particular the proof principle of complete induction, the concept of functions, dealing with complex numbers and the convergence of sequences and series. They know central concepts of differential and integral calculus in one and several variables

Use, application and generation of knowledge

Students are able to apply mathematical methods to analyze and solve problems. They can differentiate and integrate functions, determine limit values and calculate them using de l'Hospital's rule, develop Taylor series and approximate functions with them. They can solve extreme value problems in one- and multi-dimensional space and determine areas and other quantities using suitable integrals. They can also confidently use complex numbers in Cartesian representation and perform arithmetic operations with them.

Communication and cooperation

Students can present mathematical facts in a professional manner, explain solutions in a comprehensible way and present mathematical arguments in writing in a structured manner.

Scientific self-image / professionalism

Students develop an understanding of mathematically precise work, in particular the importance of formal proofs, systematic problem analysis and precise argumentation, and apply these principles responsibly in their studies.

Number ranges, full induction

Functions: Polynomials, rational functions, exponential and logarithmic functions, trigonometric functions and their inverse functions, and other elementary functions

Convergence of sequences and series

Limit values and continuity of functions, calculation of zeros of functions

Differentiability of functions; one- and multidimensional differential calculus

Rule of de l'Hospital

Taylor series expansion, approximation of functions by polynomials

Local and global extrema of functions in one or more variables

Integration of continuous functions in one and more variables (antiderivative, partial integration, substitution rule)

Teaching methods

Lecture in interaction with the students

lecture-accompanying exercise

active, self-directed learning through tasks and accompanying materials

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of a written exam in which students should recall and remember basic knowledge of the content covered. In addition, they should be able to transfer and apply this knowledge to new issues.
Duration: 90 minutes.

Requirements for the awarding of credit points

The performance is graded and must be completed with at least sufficient (4.0).

The performance is considered at least sufficient if at least 50% of the possible points are achieved in both the basic part and the entire examination.

Applicability of the module (in other degree programs)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Literature

Forster, O.: Analysis 1, Wiesbaden, Springer Spektrum, 2023, 13. Auflage.

Forster, O.: Analysis 2, Wiesbaden, Springer Spektrum, 2025, 12. Auflage.

Papula, L.: Mathematik für Ingenieure und Naturwissenschaftler Band 1 , Wiesbaden, Springer Vieweg, 2024, 16. Auflage.

Papula, L.: Mathematik für Ingenieure und Naturwissenschaftler Band 2 , Wiesbaden, Springer Vieweg, 2025, 15. Auflage.

Teschl, G. & Teschl, S.: Mathematik für Informatiker Band 2, Wiesbaden, Springer Vieweg, 2014, 3. Auflage

Technisches Englisch
PF

2 SWS

2.5 ECTS

Number
41102
Language(s)
en
Duration (semester)
1
Contact time
30 h
Self-study
45 h

Learning outcomes/competences

After successful completion of the module students will be able to:

Knowledge and understanding

name and explain key technical vocabulary from IT and technology .
describe technical objects, systems and processes precisely in Englishhe describe.

Use, application and generation of knowledge

structure technical content appropriately for the target group(introduction - main part - conclusion) and transfer it into an understandable presentation .
Suitable visualizations (e.g. diagrams/tables) to support technical statements statements use.
Summarize technical information concisely together (e.g. abstract/handout/slide-text) and put them into presentation materials integrate.

Communication and cooperation

present technical content correctly and comprehensibly in English .
an English-language technical discussion lead by asking questions, arguing and giving feedback.

Scientific self-image / Professionalism

Fundamental principles of scientific work in English apply by citing and citing sources correctly.
the own linguistic and technical presentation reflect and further develop this with the help of feedback develop.

Basics of technical English: Technical vocabulary, typical formulations, description of technical facts.

Presentation techniques: Structure/outline, linguistic means, presentation phrases, use of visual aids. visual aids

Scientific work: Source work, citation techniques, precise summaries of technical content. content.

Discussion techniques: Questions/answers, argumentation, feedback, role plays/exercises on IT topics.

Practical application: Presentations on technical IT topics during the semester

Teaching methods

Seminar-style teaching in English language with activating phases.

Oral and written exercises on technical technical description and terminology.

Presentation workshops (preparation, implementation, feedback).

Discussions/role-playing games on current IT topics.

Independent research and development of presentation content. presentation content.

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

R (presentation / presentation), ungraded (pass / fail)

Competence-oriented Description of the examination:
With the presentation, students demonstrate that they can present technical content in a technically correct, structured and target group-oriented manner in English and can answer questions in a short technical discussion.

Duration: 10-15 minutes presentation + subsequent Q&A session
Evaluation criteria (pass/fail) passed): Professionalism, comprehensibility, linguistic accuracy, presentation technique accuracy, presentation technique

Requirements for the awarding of credit points

Participation in the placement test before the semester.

Passed semester presentation (10-15 minutes) with Q&A session.

Minimum attendance: At least 80 % of the appointments (usually corresponds to max. 20 % missing appointments); required, as learning objectives can only be achieved through continuous practice, presentation and discussion. If the minimum attendance is not met without excuse, the preliminary examination work is deemed not to have been completed. As a result, the module will be graded as "failed" (NB).

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

Williams, E., Kleinschroth, R., Courtney, B. (2025). "Matters Technik - IT Matters 3rd Edition - Revised: B1-C1 - Englisch für technische Ausbildungsberufe". Cornelsen Verlag. ISBN-13: 978-3-06-452538-2

Datenbanken 1
PF

4 SWS

5 ECTS

Number
43052
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the module courses and completion of the course-related project, students will be able to use relational databases for data management. Subject and methodological competence:

Know the tasks of a database management system, the ACID transaction concept and the schema architecture of a DBMS.
Know and apply SQL commands for setting up, storing and querying data (DDL, DML, DRL, DCL)
Implement simple user views, stored functions and triggers.

Social skills:

Developing, communicating and presenting relational models and database programs in small groups
Collaboratively creating and evaluating learning posters or review questions on the course content.

Professional field orientation:

Know the requirements of different job profiles in the database environment (database administrator. Database developer, application developer, data protection officer)

Database and transaction concept

Relational model and relational algebra

Normalization of relations

SQL Data Definition Language and database integrity

SQL Data Manipulation Language

SQL Data Retrieval Language

SQL Views

Roles and rights management

Stored functions and triggers

Teaching methods

Solving practical exercises in individual or team work

Processing programming tasks on the computer in individual or team work

Active, self-directed learning through tasks, sample solutions and accompanying materials

Exercises or projects based on practical examples

Mini-exams during the semester for regular feedback

The lecture is offered as a video

Inverted teaching (inverted classroom)

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of a written exam (60-90 minutes) and coursework during the semester.
In the written exam, students should demonstrate basic knowledge of the use of databases and their skills in using SQL to solve application problems.

Through coursework during the semester, students should model and implement an application scenario of their own choice.

Requirements for the awarding of credit points

The performances are graded and must be passed with a total grade of 4.0. The performances consist of:

passed written examination (80%-100%)
successful internship project (project-related work) (0%-20%)

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

Beighley, L., SQL von Kopf bis Fuß, O'Reilly, 2008.

Kemper, A., Wimmer, M.; Übungsbuch Datenbanksysteme, Oldenbourg; 2. aktualisierte Auflage, 2009.

Saake, G., Sattler, K., Heuer A., Datenbanken - Konzepte udn Sprachen, 6. Auflage, mitp, 2018.

Lern- u. Arbeitstechniken
PF

2 SWS

2.5 ECTS

Number
411031
Language(s)
de
Duration (semester)
1
Contact time
30 h
Self-study
45 h

Learning outcomes/competences

Interdisciplinary methodological competence:

After successful participation in the module courses, students are able to understand standards and procedures in the field of learning and working techniques (including time and self-management, learning theory, communication and effective collaboration as well as creativity techniques) and apply them to their studies across disciplines.

Self-competence:

After successfully completing the module courses, students will be able to apply learning methods, communication and presentation techniques, creativity and problem-solving techniques, time and self-management methods and the basics of academic work to their studies and career.

Social skills:

After successful participation in the module courses, students are able to apply techniques of effective cooperation and problem-solving techniques in groups.

The course includes modules on the following topics:

Time management
Self-management
Motivation
Burnout
Creativity
Problem solving techniques
Effective collaboration
Learning types
Basics of scientific work
Mentoring discussions (include questions of study choice, study organization, individual time and learning planning, dealing with difficult situations and preparation for internships)

Teaching methods

Seminar-style teaching with flipchart, smartboard or projection

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Homework at the end of the semester [100%] (pass or fail)
Attendance in at least 80% of the modules of the course

Reason for the attendance obligation

The course should enable students to apply various learning, work, communication and self-management techniques in their studies and everyday professional life. Due to their nature, learning these skills requires both intensive cooperation with and personal guidance from the respective lecturers, as well as a large amount of practical work in the group under active supervision by the lecturers. In order to achieve these goals, a minimum attendance requirement is necessary in this course.

Requirements for the awarding of credit points

Passed term paper

Participation in at least 80% of the modules of the course

Participation in the mentoring program

Reason for the participation obligation

The course should enable students to apply various learning, work, communication and self-management techniques in their studies and everyday professional life. Due to their nature, learning these skills requires both intensive cooperation with and personal guidance from the respective lecturers, as well as a variety of practical work in the group under active supervision by the lecturers. In order to achieve these goals, a minimum attendance requirement is necessary in this course.

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

Friedrich Rost; Lern- und Arbeitstechniken für das Studium; Vs Verlag 6. Auflage 2010; ISBN-13: 978-3531172934

Mathematik für Informatik 3
PF

4 SWS

5 ECTS

Number
42073
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Acquisition of basic knowledge of applied statistics and the ability to select and apply descriptive and inductive statistical methods to solve problems of practical relevance.

Technical and methodological competence:

Acquisition of methodological basics of descriptive and inferential statistics
Describing essential structures in data by selecting suitable descriptive means
Converting problems into random variables and suitable distribution assumptions
Drawing inferences from samples to populations using parameter and interval estimation
Formulation of test problems and independent implementation of hypothesis tests
First experience with the computer-aided analysis of data

Interdisciplinary methodological competence:

Supporting decision-making processes through descriptive data analysis and statistically sound statements
Transferring estimation and test procedures to problems in computer science
Applying statistical methods in connection with the evaluation of databases
Simulation of stochastic processes with the help of theoretical distributions
Derivation of forecasts with the help of statistical estimation methods

Empirical frequency distributions and graphical representations

Location measures, measures of dispersion and box plots

Measures of correlation and exploratory regression

Concept of probability, random events, Laplace model

Combinatorics

Conditional probability, independence of events, Bayes' theorem

Distribution and parameters of discrete random variables

Equal distribution, binomial distribution, hypergeometric distribution

Distribution and parameters of continuous random variables

Equal distribution, normal distribution, central limit theorem

Point estimators and their properties

Confidence intervals for expected value and proportion value

Testing hypotheses, binomial test, Gaussian test, t-test

Independent computer-aided analysis of data sets, e.g. in Excel. Python or R

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving practical exercises in individual or team work

Processing programming tasks on the computer in individual or team work

Active, self-directed learning through internet-supported tasks, sample solutions and accompanying materials

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Literature

Fahrmeir et al.; Statistik: Der Weg zur Datenanalyse; Springer; Berlin Heidelberg; 8. Auflage; 2016

Vorlesungsskript

Objektorientierte Programmierung und Datenstrukturen
PF

5 SWS

5 ECTS

Number
42012
Language(s)
de
Duration (semester)
1
Contact time
75 h
Self-study
75 h

Learning outcomes/competences

After successfully completing this module, students will be able to:

Knowledge and understanding:

Explain the concepts of objects, classes, associations, and inheritance.
Describe the principles of interfaces and polymorphism.
Interpret UML class diagrams and object diagrams.
Explain the properties and functionality of lists, binary trees, AVL trees, B-trees, uand hashing.
Explain key concepts of graphs

Use, application and generation of knowledge:

Implement objects and classes in an object-oriented programming language.
Implement UML class diagrams in an object-oriented language.
Apply and implement algorithms for efficient use of lists, trees and hashing.
Use given algorithms and data structures, such as collections in Java, to solve problems
Apply simple graph algorithms such as depth-first and breadth-first search, topological sorting, minimum spanning trees and shortest paths

Communication and cooperation:

Develop smaller object-oriented software projects in teams.
Document and present program code and concepts to fellow students and instructors in an understandable way.

Scientific self-image / professionalism:

Analyze simple algorithms and software structures for efficiency.
Reflect on the relevance of algorithms and data structures for software development.
Apply the principles of object-oriented programming systematically.

Object-oriented concepts: objects, classes, associations, inheritance, interfaces, polymorphism

UML: Class diagrams and object diagrams

Data structures: lists, binary trees, AVL trees, B-trees, hashing

Graphs and simple graph algorithms (e.g. depth-first search, breadth-first search, topological sorting, minimum spanning trees, shortest paths)

Practical implementation in an object-oriented programming language (e.g. Java)

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Exercise accompanying the lecture

Internship accompanying the lecture

Group work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

D. Ratz, D. Schulmeister-Zimolong, D. Seese, J. Wiesenberger, Grundkurs Programmieren in Java, 9. Auflage, Hanser, 2024

C. Ullenboom, Java ist auch eine Insel, 17. Auflage, Galileo Press, 2023

A. Solymosi, U. Grude, Grundkurs Algorithmen und Datenstrukturen in JAVA, Springer Vieweg 2017

R. Sedgewick, K. Wayne, Algorithmen: Algorithmen und Datenstrukturen, 4. Auflage, Pearson Studium 2014

Objektorientierte Programmierung und Datenstrukturen – Projektwoche
PF

2 SWS

2.5 ECTS

Number
42013
Language(s)
de
Duration (semester)
1
Contact time
30h
Self-study
45h

Learning outcomes/competences

After successfully completing this module, students will be able to

Knowledge and Understanding

Locate the theoretical concepts of object orientation (encapsulation, inheritance, polymorphism) in the context of a more complex application architecture.
Weigh up the advantages and disadvantages of different data structures (lists, sets, maps, trees) for specific use cases.
Understand the structure and life cycle of a complete console application.

Use, apply and generate knowledge

Design and implement an executable console application independently based on a textual task.
Select and correctly apply suitable standard data structures for the efficient storage and processing of data
Write robust code that catches input errors and accounts for edge cases.
Use development tools (IDE, debugger) routinely to systematically find and fix logical errors in the program flow.

Communication and cooperation

Defining work packages in small groups (teams),
coordinating interfaces between program components and coordinating the integration of subcomponents.
Resolve conflicts and discuss solutions constructively when working together on source code
To justify own implementation decisions to team members in a professional manner.

Scientific self-image / professionalism

To realistically estimate time resources within the framework of a fixed deadline (5-day block) and to adapt project management accordingly (timeboxing).
Sticking to the principles of "clean code" (readability, maintainability, meaningful commenting) even under time pressure.
Critically reflect on whether the chosen software architecture meets the requirements or whether refactoring is necessary.

The project week is held as a five-day block course following the lecture "Object-oriented programming and data structures" and includes the development of console applications for given tasks in individual and team work. In-depth knowledge of the contents of "Object-oriented programming and data structures" is assumed.

Teaching methods

Processing programming tasks on the computer in teamwork.

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of a project-related programming assignment in a team with a presentation and a subsequent oral examination. The performance is not graded.
Duration of the oral examination: 15 - 20 minutes.

Requirements for the awarding of credit points

In order to enable teamwork and to be able to accompany the professional creation of the programs by the teachers, a minimum attendance requirement with active participation of 80% is required.

Recognizable personal contribution to the code created in the team, appropriate to the scope.

Passing the oral exam.

Applicability of the module (in other degree programs)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Literature

D. Ratz, D. Schulmeister-Zimolong, D. Seese, J. Wiesenberger, Grundkurs Programmieren in Java, 9. Auflage, Hanser, 2024
C. Ullenboom, Java ist auch eine Insel, 17. Auflage, Galileo Press, 2023
A. Solymosi, U. Grude, Grundkurs Algorithmen und Datenstrukturen in JAVA, Springer Vieweg 2017
R. Sedgewick, K. Wayne, Algorithmen: Algorithmen und Datenstrukturen, 4. Auflage, Pearson Studium 2014

Rechnerstrukturen und Betriebssysteme 2
PF

4 SWS

5 ECTS

Number
42032
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students will be able to:

Knowledge and understanding

Understand the basic components of an operating system (process and thread management, mechanisms for communication and synchronization) and explain how they work
Know advanced aspects of computer structures such as multiprocessor systems and be able to outline their implications for operating systems by way of example.

Use, application and generation of knowledge

To implement concurrent applications with processes and threads and to select unified means of communication and synchronization
Implement simple system programs using system calls.
Detect and avoid the potential problems of concurrent programs (e.g. race conditions or deadlocks)
In-depth practical application of the Linux operating system, especially when using the command line

Communication and cooperation

Successfully complete programming and analysis tasks in groups of two and
communicate technical contexts from the areas of computer structures and operating systems in an understandable way.

Scientific self-image / professionalism

Critically reflect on concepts of computer technology and operating systems in a technical and social context.
to independently acquire further knowledge in the field of computer technology and operating systems.

How to deal with operating systems on the command line

Operating system programming (C, JAVA and Java Native Interface)

Threads (thread model, comparison to processes, threads in Linux and Windows)

Communication (pipes, FIFOs, semaphores, shared memory, sockets, RPC)

Synchronization of processes and threads (mutual exclusion, conditional synchronization, rendezvous with semaphores and monitors)

Input and output (hardware, interrupt, DMA, driver)

Multiprocessor systems (hardware, scheduling, synchronization)

Virtual machines (overview of machine types, JavaVM as a virtual stack machine, instruction set of JavaVM)

Case study (e.g. Linux/Android, Windows)

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

lecture-accompanying exercise
Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written exam paper [length: 100%] (90min); semester-long examination (bonus points)

Requirements for the awarding of credit points

Pass a 90-minute graded written exam with at least sufficient (4.0)

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor of Computer Science Dual

Literature

Tanenbaum, A.S.; Bos, H.; Moderne Betriebssysteme; Pearson Studium; 2016

Stallings, W.; Operating Systems; Pearson, 2017

Glatz, R.; Betriebssysteme; dpunkt.verlag, 2019

Tanenbaum, A.S.; Austin, T.; Rechnerarchitektur; Pearson Studium, 2014

Studium Generale
PF

2 SWS

2.5 ECTS

Number
411033
Duration (semester)
1
Contact time
30 h
Self-study
45 h

Learning outcomes/competences

In this module, students can choose from a selection of cross-university courses. The competencies are defined by the respective course.

In this module, you can choose from a selection of cross-university courses. The content is defined by the respective course.

Teaching methods

In this module, students can choose from a selection of cross-university courses. The forms of teaching are defined by the respective course.

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

In this module, students can choose from a selection of cross-university courses. The forms of examination are defined by the respective course.

Requirements for the awarding of credit points

In this module, students can choose from a selection of cross-university courses. The prerequisites are defined by the respective course.

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

3. Semester of study

Datenvisualisierung
PF

4 SWS

5 ECTS

Number
43460
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students know the specialist terminology of data visualization and can use it correctly to describe data visualization problems and systems. They will know essential data structures and methods of data visualization. They know the architecture of common visualization systems.
You will be able to select an appropriate visualization method based on the properties of the data and the visualization goal and use it with R software, among other things.

The module teaches the basics of data description through visualization. The visualization process, basic visualization techniques, historical developments and the human influence factor are discussed. Methods for visualizing scalar data, temporal data and geographical-spatial data are taught as well as graph visualizations and interaction techniques.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection, solving practical exercises in individual or team work, working on programming tasks on the computer in individual or team work, exercises or projects based on practical examples

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Oral examination

Requirements for the awarding of credit points

passed oral examination

Applicability of the module (in other degree programs)

Bachelor's degree in computer science

Literature

Schumann, H., Müller W.: Visualisierung, 1. Auflage, Springer Verlag, 2000
Ward M., Grinstein G., Keim D.: Interactive Data Visualization, 2nd ed., CRC Press, 2015
Tominski C., Schumann H.: Interactive Visual Data Analysis, CRC Press, 2020

IT-Recht
PF

2 SWS

2.5 ECTS

Number
45202
Language(s)
de
Duration (semester)
1
Contact time
30 h
Self-study
45 h

Learning outcomes/competences

After participating in this module, students will be able to

Knowledge and understanding

Explain the basics of civil law (BGB), copyright law and data protection (GDPR) in the context of IT.
Describe the phases of IT contractual relationships (initiation, execution, termination) and understand their legal consequences.
Explain the system of liability for breaches of duty and the difference between warranty and guarantee.
Outline the legal framework of cloud computing, open source software and e-commerce.
Name the importance of compliance requirements and the IT Security Act for business practice.

Use, apply and generate knowledge

Analyze technical issues independently and transfer them to the existing legal environment (subsumption).
Check general terms and conditions (GTC) in the B2B and B2C sector for obvious ineffectiveness.
Risks in IT projects (e.g. in the case of BYOD, third-party software). risks in IT projects (e.g. BYOD, use of third-party software) and develop legally compliant design alternatives.
apply data protection regulations to technical architectures (privacy by design).
select licensing models for software (in particular open source vs. proprietary) and integrate them into software projects in a license-compliant manner.
Distinguish between standard cases that can be resolved independently and complex issues that require qualified legal assistance (boundary recognition).

Communication and cooperation

Discuss legal requirements for software solutions in interdisciplinary teams (technology/law) and develop solutions.
In order to explain technical requirements to lawyers or management in an understandable way and to create documentation for compliance purposes.

Scientific self-image / professionalism

Design result-oriented technical processes and developments in a legally robust manner.
To critically reflect on the consequences of legal standards for one's own technical work and project management and to act ethically and responsibly.

Contract initiation and conclusion

Other terminology

IT law and general terms and conditions

Teaching methods

Lecture in seminar style, with blackboard writing and projection

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Oral examination

Requirements for the awarding of credit points

passed oral examination

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

IT- und Computerrecht, Gesetzessammlung, Beck-Texte im dtv;
Telekommunikations- und Multimediarecht, Beck-Texte im dtv;

jeweils in der aktuellen Ausgabe

Mathematik für Informatik 4 Data Science
PF

4 SWS

5 ECTS

Number
43076
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students will be able to explain and apply advanced mathematical methods, especially from the fields of statistics and linear algebra, with regard to data science methods. Students will be able to make well-founded decisions about which techniques should be used to solve which problems, with the specific issues addressed coming primarily from the areas of fast algorithms, data analysis and compression, image processing and network analysis. They can map and analyze application problems represented by graphs using matrices.

The module course teaches the calculation and properties of eigenvalues and vectors, special graphs and matrices, norms of vectors and matrices. For data analysis and compression, singular value decomposition, principal components, pseudoinverse, maximum likelihood estimators, linear models and logistic regression are covered. In addition, algebraic structures (groups, solids, polynomial rings) and the basics of number theory are taught.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection, solution of practical exercises in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in computer science

Literature

B. Lenze, Basiswissen Lineare Algebra, Springer Vieweg Verlag, Wiesbaden, 2020, zweite Auflage.

G. Strang, Linear Algebra and Learning from Data, Wellesley-Cambridge Press, Wellesley, USA, 2019

V. Turau und Ch. Weyer, Algorithmische Graphentheorie, De Gruyter, Berlin, 2015

M. Aigner, Diskrete Mathematik, Vieweg Springer-Verlag, Berlin-Heidelberg, 2006

Mensch Computer Interaktion
PF

4 SWS

5 ECTS

Number
43081
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

The course teaches the basics of user interfaces for efficient cooperation and interaction between humans and computers. In this context, both physiological and psychological aspects of human information processing are covered. Furthermore, software ergonomics is introduced as a scientific field that deals with the design of human-machine systems. Furthermore, the effects on concepts and implementations of software systems and user interfaces are examined and discussed.

Technical and methodological competence:

Observation of the basic learning and action processes when using software
Knowledge of the standard operating elements for WIMP interfaces
Name the most important standards, laws and guidelines on SW ergonomics
Fundamental evaluation of the ergonomics of user interfaces based on these regulations
Mapping the activities in the user-centered design process to case studies
Basic knowledge of the most important usability engineering tools and their application in case studies

Interdisciplinary methodological competence:

Knowledge of simplified action process models

Social skills:

Observation, assessment and evaluation of communication situations
Working on tasks in alternating small groups (2-4 students each)

Professional field orientation:

Interdisciplinarity of user experience design
Application of simple usability engineering tools (e.g. personas) using a case study

1. basics

Introduction and motivation
Definition of software ergonomics
Perception
Memory and experience
Processes of action
Communication

2. implementation

Norms and laws
Guidelines
Hardware
Forms of interaction
Graphical dialog systems

3. user-centered design

Introduction
Web usability
Accessibility
Tools of usability engineering

4. further contents

In consultation with the students, one to three of the following topics will be covered. The list will be expanded as required

Gesture control

User interfaces in computer games

User interfaces for mobile systems

Brain-computer interfaces

Multitouch interfaces

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving practical exercises in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The form of examination used in the respective semester (e.g. oral examination) will be announced at the beginning of the course. This also applies to any bonus points regulations that may be used.

written written examination

project work with oral examination

study achievements during the semester (bonus points)

Requirements for the awarding of credit points

passed written examination

passed oral examination

successful project work

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Literature

B. Lenze, Basiswissen Lineare Algebra, Springer Vieweg Verlag, Wiesbaden, 2020, zweite Auflage.

G. Strang, Linear Algebra and Learning from Data, Wellesley-Cambridge Press, Wellesley, USA, 2019

V. Turau und Ch. Weyer, Algorithmische Graphentheorie, De Gruyter, Berlin, 2015

M. Aigner, Diskrete Mathematik, Vieweg Springer-Verlag, Berlin-Heidelberg, 2006

Programmierkurs Anwendungsentwicklung
PF

4 SWS

5 ECTS

Number
42021
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Providing the knowledge required to implement application software from a professional point of view. This includes the realization of graphical user interfaces, the connection of technical concept classes and the persistence of data. Concepts of object-oriented programming are applied in a problem-oriented manner.

Technical and methodological competence:

Implementing flexible systems through the use of polymorphism and interfaces
Recognizing the advantages of regulated exception handling
Implementing a flexible graphical user interface using components and layout managers
Using data streams
Identifying and solving concurrent programming problems
Reusing components via the targeted use of an application programming interface (API)

Interdisciplinary methodological competence:

Application of programming techniques in the implementation of commercial, technical and multimedia applications

In-depth study of object-oriented programming in Java (abstract classes, interfaces, polymorphism)

Professional exception handling via exceptions

Use of collections for object management

Access to the file system and organization of files (Java IO)

Use of data streams

Serialization of objects

Programming graphical user interfaces (JavaFX)

Event handling

Concurrent programming (threads)

Java Stream API and lambda expressions

Architecture of application programs from an implementation perspective

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving practical exercises in individual or team work

Processing programming tasks on the computer in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Literature

Horstmann, C., Cornell, G.; "Core Java, Volume 1: Fundamentals", Pearson, Boston, 2018

Horstmann, C., Cornell, G.; "Core Java, Volume 2: Advanced Feature", Prentice Hall, Boston, 2016

Krüger, G., Hansen, H.; "Java-Programmierung - Das Handbuch zu Java 8", O'Reilly Verlag, Köln, 2014

Urma, R.-G., Fusco, M., Mycroft, A.; "Java 8 in Action: Lambda, streams, and functional-style programming", Manning, 2015

Epple, A.; "Java FX 8", dpunkt.verlag, Heidelberg, 2015

Sharan, K.; "Learn JavaFX8", Apress, Springer Science, New York, 2015

Sierra, K., Bates, B.; "Head First Java", O'Reilly, 2005

Programmierkurs Python und R
PF

4 SWS

5 ECTS

Number
43021
Language(s)
de, de_en
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the module courses and completion of the project work, students are able to ...

Knowledge and understanding:

Understand the basic concepts of the Python and R programming languages

Use, apply and generate knowledge:

Use the Python and R programming languages practically for analysis
to realize programs in Python and R and to implement first data science methods on sample data
implement advanced programs for data analysis from data acquisition to analysis and visualization with established libraries

Introduction to the programming languages Python and R:

Peculiarities of dynamically typed languages
Structured and object-oriented programming
Control structures
Fundamental data types
Visualization
Use of libraries from the data science environment
Use of typical tools

Special features of the Python programming language
Special features of the R programming language

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Processing programming tasks on the computer in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

A written exam (90 - 120 minutes) consisting of two parts: Programming with R (50% of the final grade) and Programming with Python (50% of the final grade)

Requirements for the awarding of credit points

passed written exam, whereby each part (programming with R and programming with Python) must be at least passed

Applicability of the module (in other degree programs)

Bachelor's degree in computer science

Literature

Kofler, M.: „Python: Der ideale Python-Einstieg für Informatikstudium, Ausbildung und Beruf“, Rheinwerk, 2024

Ernesti, J. und Kaiser, P.: „Python 3: das umfassende Handbuch“, Rheinwerk, 2023

VanderPlas, J.: "Python Data Science Handbook", O Reilly, 2017

Ligges, U.: "Programmieren in R", Springer, 2009

Wickham, H. und Grolemund, G.: "R für Data Science", Heidelberg, O'Reilly, 2017

Seminar (Methodik)
PF

2 SWS

2.5 ECTS

Number
451811
Language(s)
de
Duration (semester)
1
Contact time
30 h
Self-study
45 h

Learning outcomes/competences

After successfully completing this module, students will be able to

Know and understand

Name and understand the competencies corresponding to the methodological focus of the seminar.

Use, apply and generate knowledge

. Apply the methodological skills corresponding to the focus of the seminar in studies and work.
Apply the methods learned in the course to an interdisciplinary topic.
Independently research and evaluate technical and scientific content.
Independently develop technical-scientific texts.
Create presentations

Communication and cooperation

Present an interdisciplinary topic to fellow students in an understandable way.
Present results.
Work in groups and interact within the groups.
Present and defend content in groups.

Scientific self-image / professionalism

Structure scientific texts independently.

The seminars include topics that expand students' interdisciplinary scientific and methodological skills. The topics are offered each semester with new, up-to-date content by all professors and are offered to students in the university's electronic information service (web) (https://fh.do/inf/seminare). Examples of courses are Presentation techniques, introduction to scientific work, planning and conducting data surveys.

Alternatively, a methodologically oriented course can be taken in the "Studium Generale" in the scope of 2 SWS. The list of selectable courses can be found in the university's electronic information service (https://fh.do/inf/generale).

Teaching methods

Seminar

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Presentation

Requirements for the awarding of credit points

Regular participation in at least 2/3 of the attendance dates

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor of Medical Informatics

Literature

Literatur muss vom Studierenden selbst ermittelt werden.

Übergreifend:

Balzert, H.; Schröder, M. und Schäfer, C.; Wissenschaftliches Arbeiten; W3l; Witten; 2. Aufl.; 2011

Softwaretechnik 1
PF

4 SWS

5 ECTS

Number
43051
Language(s)
de
Duration (semester)
1
Contact time
60h
Self-study
90h

Learning outcomes/competences

Introduction to the implementation of software projects with a special focus on the early phases of development and modeling of software-based solutions with the help of creative methods (e.g. design thinking) and the methods of requirements engineering. Consideration of the integration of AI-based modules in the development process and in the design of the software project, taking into account social implications and regulatory framework conditions.

Modeling of the software system with the Unified Modeling Language (UML) and Domain Driven Design (DDD) methods. Knowledge of various process models and practical experience with agile methods such as Scrum.

Technical and methodological competence:

Overview of procedure and process models of software development
Name and apply various requirements engineering methods
- Differentiate, specify and formulate user and system requirements
- Verifying and validating requirements
Overview of the consequences of digitalization and digital transformation with a special focus on the effects in the area of software engineering

Knowing and applying innovation methods
Be able to integrate AI-based modules into the development process

a) Impact on the development process
b) Consideration of regulatory framework conditions
c) Analysis of social implications

Describe the methodological approach in object-oriented analysis
Know and apply the relevant UML description tools in the context of OOA

UML use case diagram
UML package diagram
UML class diagram
UML activity diagram
UML sequence diagram
UML communication diagram
UML state diagram

Interdisciplinary methodological competence:

Modeling the static and dynamic aspects of an OOA model for an object-oriented software system to be developed
Object-oriented specification of software systems using the Unified Modeling Language (UML)
Creation of a technical concept or product model for a software system
Recognizing contradictions, incompleteness, inconsistencies

Social skills:

Systematically analyze problems of medium complexity in a team
Develop a requirements specification in a cooperative and collaborative team
Specify an OOA model for a software system in a cooperative and collaborative team

General basics of software engineering (motivation, definitions, goals,...)
Procedure models (classic to agile)
Fundamental terms, phases, activities and procedures in the context of requirements engineering
Digitalization, change and creative methods in the context of software engineering
Peculiarities of the integration of AI-based modules
Fundamental terms, methods and notation in the context of object-oriented analysis (OOA) and domain-driven design (DDD)
Object-oriented analysis with UML (including use cases, packages, activity diagram, class diagram, state diagram, scenario)
Analysis patterns, static/dynamic concepts and sample applications
Checklists for the OOA model
Components and contents of the OOA documentation

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Internship to accompany the lecture

Project work accompanying the lecture with final presentation

Workshops

Group work

Individual work

Case studies

Excursion

Project work

The lecture is offered as a video

Inverted classroom teaching

Concluding presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Oral examination

Project work with oral examination

Homework

Presentation

Requirements for the awarding of credit points

successful project work

successful term paper

successful presentation

successful internship project (project-related work)

participation in at least 90% of the attendance dates for exercise and internship

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

Balzert, H. (2009): Lehrbuch der Softwaretechnik - Basiskonzepte und Requirements Engineering (3. Aufl.), Heidelberg: Spektrum Akademischer Verlag.

Ludewig, J.; Lichter, H. (2013): Software Engineering - Grundlagen, Menschen, Prozesse, Techniken, 3. korrigierte Auflage, Heidelberg: dpunkt-Verlag.
Oestereich, B., Scheithauer, A. (2013): Analyse und Design mit UML 2.5, 11. Auflage, München: Oldenbourg Verlag.
OMG (2017): UML Specification Version 2.5.1, http://www.omg.org/spec/UML/2.5.1/PDF.
Pichler, R. (2008): Scrum, Heidelberg: dpunkt-Verlag.
Pohl, K., Rupp, C. (2015): Basiswissen Requirements Engineering, 4. überarbeitete Auflage, Heidelberg: dpunkt-Verlag.
Rupp et. al. (2012): UML 2 glasklar. 4. Auflage, Hanser-Verlag.
Sommerville, I. (2012): Software Engineering, 9. Auflage, München: Pearson Studium.

Begründung zur Teilnahmeverpflichtung

Die Studierenden erarbeiten in Teamarbeit sowohl kreative Lösungen als auch formale Beschreibungen für konkrete Fragestellungen und UseCases aus der Industrie. Dabei werden Sie von den Lehrkräften begleitet und gecoacht. Um die dabei gemachten Erfahrungen zu analysieren und die sich daraus ergebenden Lernziele zu erreichen ist eine Mindestanwesenheitspflicht im Praktikum erforderlich.

4. Semester of study

Data Science Datenbanken
PF

4 SWS

5 ECTS

Number
44270
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the module courses, students will be able to ...

Know and understand:

Explain and use data exchange formats.

Discuss the functionality of ETL and ELT processes.

Discuss the advantages and disadvantages of column-oriented versus row-oriented storage formats.
Differentiate between data warehouses and data lakes and assess their functionality and areas of application.
Discuss the advantages and disadvantages of column-oriented versus row-oriented storage formats.
Explain the challenges of big data and identify alternative solution concepts.
Explain different NoSQL databases and compare their areas of application.

Deploy, apply and generate knowledge:

Develop EER models and transfer them to relational and non-relational databases.

Perform performance optimization using examples.

Implement ELL and ELT processes.

Perform OLAP operations.

Communication and cooperation:

work on a problem together in small groups.

Scientific self-image / professionalism:

Document and present project results.

General

Data modeling, logical database design
Data exchange formats

Relational databases

Memory access structures
Performance optimization and SQL tuning

Data warehouse technologies

ETL & ELT processes (data integration and data migration)
OLAP, column orientation, multidimensional analysis
Data lakes

NoSQL databases

Basics and challenges of big data
Limitations of relational databases
CAP theorem
Scaling of databases (horizontal / vertical)
Examples of NoSQL databases (e.g. document-oriented databases, time-series databases, vector databases)

Teaching methods

Lecture in seminar style, with blackboard writing and projection

Internship to accompany the lecture

Processing programming tasks on the computer in individual or team work

project work accompanying the lecture with final presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination is passed

80% of the overall grade consists of a written examination (60-90 minutes) or oral examination (20-30 minutes) (according to the current examination schedule), in which students analyze application scenarios, explain various theoretical principles and apply them situationally and
to 20% of the overall grade from an examination during the semester (project work with final presentation and/or elaboration), with which students should demonstrate that they can apply, reflect on and evaluate the technologies learned in the lecture

Requirements for the awarding of credit points

Passed written exam or oral exam and internship project (project-related work)

Applicability of the module (in other degree programs)

Bachelor's degree in computer science

Literature

A. Bauer, H. Günzel, Data Warehouse Systeme, 2009
R. Elmasri, S. Navathe, Grundlagen von Datenbanksystemen, 2009
G. Saake, K.-U. Sattler, A. Heuer, Datenbanken Implementierungstechniken, 2011
R. Niemiec, Oracle database 12c release 2 performance tuning tips & techniques, 2017
R. Panther, SQL-Anfragen optimieren, 2014
S. Edlich, A. Friedland, J. Hampe, B. Brauer, NoSQL Einstieg in die Welt nichtrelationaler Web 2.0 Datenbanken, Hanser Verlag 2010
M. Kleppmann, Designing data-intensive applications, O'Reilly Media (2017)

Weitere aktuelle Literatur wird in der Vorlesung bekannt gegeben.

Grundlagen des Maschinellen Lernens
PF

4 SWS

5 ECTS

Number
45470
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the course, students have detailed skills in the application and analysis of machine learning methods and are able to..:

use the most important concepts of machine learning to explain learning systems
know and apply the methods of data preprocessing
select feature engineering methods appropriate to the problem
select, apply and interpret suitable machine learning methods for specific applications
comparatively evaluate the performance of machine learning methods
describe the criteria for the trustworthiness of machine learning methods
to explain the results of machine learning methods
to use the R programming language, in particular the mlr3 package, for machine learning

CRISP-DM data mining process
Categories of machine learning
Data preprocessing
-   Data description
-   Missing and incorrect data
-   Feature engineering
Unsupervised learning
-   Distance measures
-   Partitioning clustering
-   Hierarchical clustering
Performance criteria and validation
-   Performance criteria for classification
-   Performance criteria for regression
-   Validation methods
Classification methods
-   Data-independent rules
-   Naive Bayes, k nearest neighbors
-   Logistic regression
-   Support Vector Machines
-   Decision Tree / random Forest / Boosting
Regression methods
-   Data-independent rules
-   Multiple linear regression
-   Other methods (regression trees, support vector regression etc.)
Dimension reduction and variable selection
-   Filter and wrapper methods
-   Principal component analysis
Trustworthy AI
-   Criteria
-   Explainability
-   Fairness
Selected further aspects of machine learning

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Internship to accompany the lecture

Processing programming tasks on the computer in individual or team work

project work accompanying the lecture with final presentation

Exercises or projects based on practical examples

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written presentation (project work accompanying the lecture with final presentation) and oral examination

Requirements for the awarding of credit points

Passed presentation and oral examination

Applicability of the module (in other degree programs)

Bachelor's degree in computer science

Literature

G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Applications in R. 2. Auflage, Springer (2021)

I. Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Auflage, Morgan Kaufmann (2017)

C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)

B. Bischl, R. Sonabend, L. Kotthoff, M. Lang (Hrsg.), Applied Machine Learning Using mlr3 in R (2024) https://mlr3book.mlr-org.com/

C. Molnar, Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. 2. Auflage (2024) https://christophm.github.io/interpretable-ml-book/

Informationssicherheit
PF

4 SWS

5 ECTS

Number
46813
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

The students are able to

define, differentiate and explain basic information security terminology.

Terminology

IT security, information security, difference between security and safety
System, fact, assumption, asset
Protection objective (CIA and authentication)
Vulnerability, vulnerability, threat, attack, attacker types
Risk
Security objective, security requirement
Security measure

Human factor, security awareness

Legal framework, European General Data Protection Regulation

Standards and best practices

ISO/IEC 27000 series
IT baseline protection
OWASP

Applied cryptography

Symmetric encryption (basics, AES, block modes, padding, pitfalls)
Hash functions (types of attack, SHA-2 family, SHA-3 family), MAC
Asymmetric cryptography (basics, DH, RSA, ECC, padding, pitfalls, digital shelf marks, certificates)

Access control

Basics (DAC, MAC, RBAC, Deny by Default, Least Privilege)
Advanced models (ABAC, ReBAC), modeling

Authentication

Basics of authentication (types, MFA, entropy)
Password-based authentication (Linux password databases, types of attacks, Salt, Argon2, NIST 800-63B)

Basics of software development and information security

Asset identification and analysis
Threat modeling
Best practices (OWASP Top 10, SAMM, ASVS, Testing Guide)
Penetration testing

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving practical exercises in individual or team work

Internship

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written exam paper

Internship

Requirements for the awarding of credit points

Passed written exam

Passed internship

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

R. Anderson: Security Engineering: A Guide to Building Dependable Distributed Systems, 3. Auflage, John Wiley & Sons Inc., 2020

C. Eckert: IT Sicherheit (Konzepte, Verfahren, Protokolle), 11. Auflage, De Gruyter Oldenbourg, 2023

ISO/IEC 27000: Information technology Security techniques Information security management systems Overview and vocabulary, 2018

K. Schmeh: Kryptografie Verfahren - Protokolle - Infrastrukturen, 6. Auflage, dpunkt.verlag, 2016

Kommunikations- und Rechnernetze
PF

4 SWS

5 ECTS

Number
46832
Language(s)
en, de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Technical and methodological competence:

After completing the course, students will be able to

Describe basic network architectures and protocols: Understand the OSI layer models and can explain how they work using specific examples.
Apply addressing and routing concepts: You understand the various IP addressing schemes (IPv4, IPv6) and can apply routing protocols (e.g. RIP, OSPF) in typical network scenarios.
Describe performance and control mechanisms at protocol level: You understand the possibilities and tasks of layer 4 protocols (TCP, UDP) and can explain existing algorithms for efficient communication
Perform network and protocol analyses: You are proficient in basic analysis and diagnostic procedures and can draw conclusions about possible problems within a network.
Configure network components and services: You can set up and administer switches, routers and server services (e.g. DHCP, DNS).
Compare transmission media and technologies: Know the advantages and disadvantages of various wired and wireless technologies (e.g. Ethernet, WIFI) and be able to evaluate them in practical applications. After completing the lecture "Communication and Computer Networks", students will be able to
Describe basic network architectures and protocols: Understand the OSI layer models and can explain how they work using specific examples.
Apply addressing and routing concepts: You understand the various IP addressing schemes (IPv4, IPv6) and can apply routing protocols (e.g. RIP, OSPF) in typical network scenarios.
Describe performance and control mechanisms at protocol level: You understand the possibilities and tasks of layer 4 protocols (TCP, UDP) and can explain existing algorithms for efficient communication
Perform network and protocol analyses: You are proficient in basic analysis and diagnostic procedures and can draw conclusions about possible problems within a network.
Configure network components and services: You can set up and administer switches, routers and server services (e.g. DHCP, DNS).
Compare transmission media and technologies: You will know the advantages and disadvantages of various wired and wireless technologies (e.g. Ethernet, WIFI) and be able to evaluate them in practical use cases.

Reference models (ISO/OSI, TCP/IP)

Bit transmission layer, transmission media

Ethernet, network components: Hub, switch, router; virtual LANs (VLAN)

IP protocols (v4 and v6), addressing, routing

Transport layer protocols, flow control,

Wireless communication

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Exercise accompanying the lecture

Editing network configurations on the computer in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written written examination

study achievements during the semester (bonus points)

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

Computer Networks
Andrew S. Tanenbaum, David J. Wetherall
6. Edition, Pearson Studium, 2021

Internetworking with TCP/IP
Douglas E. Comer
6. Auflage, Pearson Studium, 2013

Wireshark® 101: Essential Skills for Network Analysis - Second Edition
Wireshark Solution Series (English Edition)
Laura Chappell, Gerald Combs, 2017

Künstliche Intelligenz
PF

4 SWS

5 ECTS

Number
46834
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module courses, students will be able to reproduce the central concepts of artificial intelligence and formal knowledge processing, explain their meaning in the context of intelligent systems and illustrate them using examples from various application areas.
After successfully completing the module courses, students will be able to describe the concepts of intelligent agents and their environments, distinguish between different types of agents, analyze their properties and explain the relevance of these concepts for practical applications.
After successful participation in the module courses, students will be able to explain the principles of adversarial games and the associated search methods (minimax search, alpha-beta pruning, expectimax) and apply them to examples.
After successful participation in the module courses, students will be able to formally define Markov Decision Processes, explain and simulate the Value Iteration Algorithm and apply it to concrete decision problems
After successfully completing the module courses, students will be able to formally describe constraint satisfaction problems, explain the associated solution methods (backtracking algorithm, forward checking, filtering methods), analyze their efficiency and apply them to specific example problems.
After successfully completing the module courses, students will be able to explain the basic concepts of machine learning, explain the differences between supervised and unsupervised learning and between classification and regression, and understand how neural networks work, including backpropagation.
After successfully completing the module courses, students will be able to identify ethical issues in the field of artificial intelligence, discuss different perspectives and challenges and critically reflect on the social impact of AI technologies.

Basic concepts of artificial intelligence and formal knowledge processing

Intelligent agents and environments and their properties

State spaces and their properties

Search methods and their properties: Breadth-first search, depth-first search, uniform cost search, heuristic search, A* search

Adversarial games, associated search methods and their properties: Minimax search, Alpha-Beta-Pruning, Expectimax

Markov Decision Processes, associated algorithms and their properties: Value Iteration Algorithm

Constraint Satisfaction Problems, associated algorithms and their properties: Backtracking Algorithm, Forward Checking, Filtering Methods (Minimum Remaining Values and Least Constraining Value)

Introduction to machine learning: supervised and unsupervised learning, classification vs. regression, basics of neural networks (structure, activation functions, loss functions, backpropagation)

KI ethics

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Exercise accompanying the lecture

Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written examination paper [scope: 100%] (90 minutes)

Semester-accompanying coursework (bonus points): Programming tasks [scope: 15%], credit only for a passed written exam paper

Requirements for the awarding of credit points

Passed written examination

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science Dual

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Business Informatics

Bachelor of Computer Science Dual

Literature

Stuart Russel, Peter Norvig: Künstliche Intelligenz. Ein moderner Ansatz ; 4. aktualisierte Auflage; Pearson; München; 2023.

Softwaretechnik 2
PF

4 SWS

5 ECTS

Number
44121
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Introduction to the topic of software architecture. Starting with terms, methods and perspectives, via architecture models in UML (distribution and component diagrams) to various architectural styles from classic to modern.

Students learn how to convert OOA and/or DDD models into an implementation. In addition to business logic and design patterns, a key focus is on the classification and targeted use of tools/frameworks from the areas of communication, persistence and interface design.

Technical and methodological expertise:

Understanding the concepts of object-oriented design
Design and documentation of applications with UML
Understand the principles, patterns and aspects of software architecture
Defining, documenting and evaluating architectures
Describing the architecture and design process
Describing and classifying modern software techniques

Interdisciplinary methodological competence:

Thinking in systems
Designing and documenting target systems
Process-oriented approach

Social skills:

Working in small teams
Results-oriented group work

General basics of software architecture (concept, motivation, definitions, goals,...)

Architecture modeling with UML (distribution and component diagram)

Architecture drivers and overview of different architecture styles

Architectural principles and views

Tier architecture, brokers, component-based architecture, SOA, microservice architectures, cloud native architectures, etc.

Object-oriented design with UML

Design patterns

Communication frameworks and tools

Databases, persistence frameworks and tools

Frameworks and tools for interface design

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Internship to accompany the lecture

Project work accompanying the lecture with final presentation

Workshops

Group work

Case studies

Excursion

Project work

The lecture is offered as a video

Inverted classroom teaching

Concluding presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Oral examination

Project work with oral examination

Homework

Presentation

Requirements for the awarding of credit points

successful project work

successful term paper

successful presentation

successful internship project (project-related work)

participation in at least 90% of the attendance dates for exercise and internship

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

Bass et al: Software Architecture in Practice, 3. Auflage, Addison Wesley, 2012.
M. Fowler: Patterns für Enterprise Application-Architekturen, 1. Auflage (Taschenbuch), mitp, 2003.
Gamma et al.: Entwurfsmuster: Entwurfsmuster als Elemente wiederverwendbarer objektorientierter Software, mitp, 2014.
C. Richardson: Microservice Patterns. 1. Auflage, Manning Publications, 2018.
Rupp et al: UML 2 glasklar, 4. Auflage, Hanser-Verlag, 2012.
G. Starke: Effektive Softwarearchitekturen: Ein praktischer Leitfaden, 9. Auflage, Hanser-Verlag,2020.
Vogel et al: Software-Architektur: Grundlagen Konzepte Praxis, 2. Auflage, Spektrum, 2009.
E. Wolff: Microservices: Grundlagen flexibler Softwarearchitekturen, 1. Auflage, dpunkt-Verlag, 2015.

Studium Generale
PF

2 SWS

2.5 ECTS

Number
451815
Duration (semester)
1
Contact time
30h
Self-study
45 h

Learning outcomes/competences

In this module, students can choose from a selection of cross-university courses. The competencies are defined by the respective course.

In this module, you can choose from a selection of cross-university courses. The content is defined by the respective course.

Teaching methods

In this module, students can choose from a selection of cross-university courses. The forms of teaching are defined by the respective course.

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

In this module, students can choose from a selection of cross-university courses. The forms of examination are defined by the respective course.

Requirements for the awarding of credit points

In this module, you can choose from a selection of cross-university courses. The prerequisites are defined by the respective course.

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor of Medical Informatics

Literature

Literatur muss vom Studierenden selbst ermittelt werden.

Übergreifend:

Balzert, H.; Schröder, M. und Schäfer, C.; Wissenschaftliches Arbeiten; W3l; Witten; 2. Aufl.; 2011

Web-Technologien
PF

4 SWS

5 ECTS

Number
46898
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Knowledge and understanding: Upon completion of this module, students will be able to

name the central basic principles and concepts of the WWW (e.g. client-server, HTTP) and the Internet (e.g. protocols) and classify them in the context of web applications,
distinguish between client-side and server-side web development techniques,
recognize basic, technology-independent architectural aspects of web applications (e.g. model-view-controller, event-driven and asynchronous programming) and transfer them to specific technologies.

Use, application and generation of knowledge: After completing this module, students will be able to

specify the structure of a web interface using HTML in a semantically correct and accessible way,
to use essential web development tools, such as development environments and build management tools,

Communication and cooperation: After completing this module, students will be able to

develop and implement solutions cooperatively in a team, and
explain and discuss their ideas and solutions, e.g. in the form of short presentations or code reviews

Scientific self-conception/professionalism: After completing this module, students will be able to

apply industry best practices in the field of web development, and
justify their technical solutions for typical tasks in web development

Module description:
In this module, students gain an overview of the central technologies of the web platform, which forms the basis of modern web applications. After completing the module, they will have mastered the central principles and concepts of these technologies and will be able to use them to implement small to medium-sized web applications for specific tasks.

Module structure:
The module covers the following topics:

Overview of the central concepts and technologies of the WWW and the Internet (e.g. client-server architecture, protocols and standards such as TCP, IP, DNS, URL, HTTP)
Client-side concepts and technologies for the development of web applications:
1. HTML (incl. semantics, accessibility)
2. CSS and responsive web design
3. JavaScript and browser APIs (e.g. DOM, AJAX)
Server-side concepts and technologies for the development of web applications:
1. Basic concepts: event-driven and asynchronous programming, request handling, modularization (e.g. with Node.js)
2. Structuring using model view controllers

Teaching methods

Flipped/Inverted Classroom:

Online e-learning materials with interactive slides and videos (asynchronous self-study)
Interactive face-to-face events for tasks and exercises based on practical examples, for additional in-depth study and for answering and discussing questions; just-in-time teaching based on accompanying questions

Project-oriented internship: project task that is worked on in teams throughout the semester

Guest lectures with experts and current topics from the industry

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written examination (scope: 100%, duration: 120 minutes); semester-related coursework (bonus points, scope: 13%)

Requirements for the awarding of credit points

Passed written exam

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Literature

Wolf, Jürgen (2023): HTML und CSS: Das umfassende Handbuch, 5. Auflage, Rheinwerk Computing
Bühler, Peter; Schlaich, Patrick; Sinner, Dominik (2023): HTML und CSS: Semantik - Design- Responsive Layouts, 2. Auflage, Springer Vieweg
Simpson, Kyle (2015-2020): You Don’t Know JS (Yet), Band 1-6, O’Reilly/Independently published
Haverbeke, Marijn (2020): JavaScript: Richtig gut programmieren lernen, 2. Auflage, dpunkt.verlag
Springer, Sebastian (2021): Node.js: Das umfassende Handbuch, 4. Auflage, Rheinwerk Computing
Tilkov, Stefan; Eigenbrodt, Martin; Schreier, Silvia; Wolf, Oliver (2015): REST und HTTP: Entwicklung und Integration nach dem Architekturstil des Web, 3. Auflage, dpunkt.verlag
Tanenbaum, Andrew S.; Feamster, Nick; Wetherall, David J. (2024): Computernetzwerke, 6. Auflage, Pearson Studium

Relevante Standards:

WHATWG (2025): HTML Living Standard, https://html.spec.whatwg.org/
W3C (2025): CSS Specifications, https://www.w3.org/Style/CSS/specs.html
Ecma International (2025): ECMA-262: ECMAScript® 2025 language specification, 16th Edition, https://tc39.es/ecma262/
WHATWG (2025): DOM Living Standard, https://dom.spec.whatwg.org

5. Semester of study

ERP 2
PF

4 SWS

5 ECTS

Number
45392
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Theoretical basic knowledge of ERP systems is taught in the course and previously acquired specialist knowledge is deepened using practical examples based on the SAP® ERP system.
The focus is initially on getting to know the structure of an ERP system, the tasks involved in selection, installation and configuration, as well as the various customization options in the ERP system (SAP® ERP®). Following on from this, the special features of maintaining and operating an ERP system are covered.
In-depth and practical implementation is carried out using a specific ERP system (SAP® ERP®). The processing of various case studies provides insights into practical and relevant aspects. In addition, basic knowledge of the ABAP® programming language is developed, taking into account database access and dialog design.

Expert knowledge:

Differentiating between standard and customized software
Naming the advantages and disadvantages of standard software
Differentiate between the various customization and expansion options of standard software and evaluate the respective consequences
Operating the ERP system as part of process case studies
Using the development environment of the ERP system
Designing and implementing functional enhancements to standard software
Transferring the knowledge acquired and developing your own solutions as part of a mini-project

Social skills:

Evaluating the importance of communication, conflict and team skills in implementation and customization projects
Sensitization to the social problems of an ERP implementation
Increasing cooperation and teamwork skills in the face-to-face exercises and mini-project

Professional field orientation:

Knowledge of the requirements of different job profiles in the ERP environment (esp. sales, consulting, project management, application development)

Technical structure of the SAP® ERP system (work processes of the application server)

Change options in SAP® ERP (types of customizations, their delimitation and consequences)

Development Workbench and its tools (ABAP® Editor, Function Builder, Screen Painter)

Meaning of the WBO (packages, requests, tasks, transport system, )

ABAP® programming language (program structure, syntax rules, declarative and operative commands)

Modularization options in ABAP® (subroutines, function modules)

Objects of the data dictionary (domains, data elements, tables)

Dialog programming (screens, PAI/PBO modules, input help, )

Inhouse developments (functional expansion of an ERP system in practical exercises based on a mini-project)

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Exercise accompanying the lecture

Solving practical exercises in individual or team work

Processing programming tasks on the computer in individual or team work

Project work accompanying the lecture with a final presentation

Group work

Individual work

Case studies

Exercises or projects based on practical examples

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Examinations during the semester (scope 1/3) + written exam (scope 2/3, duration: 60 minutes)

Requirements for the awarding of credit points

Semester examinations and written examinations must be passed in total.

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Literature

Färber, Günther; Kirchner, Anja (2008): ABAP - Grundkurs. 4. Auflage. Galileo Press.

Keller, Horst; Krüger, Sascha (2006): ABAP Object: ABAP-Programmierung mit SAP NetWeaver. 3. Auflage. Galileo Press.

Kühnhauser, Karl-Heinz (2005): Einstieg in ABAP. Galileo Press.

Data Science Projekt
PF

4 SWS

5 ECTS

Number
45475
Language(s)
en, de, de_en
Duration (semester)
1
Contact time
60h
Self-study
90h

Learning outcomes/competences

After successful participation in the module courses and completion of the project work, students will be able to ...

Use, application and generation of knowledge:

- Apply theoretical, conceptual and practical knowledge and skills of data science
. - carry out projects in the field of data science and familiarize themselves with a specific topic and deepen the relevant topics independently.

Communication and cooperation:

- to work in small groups and to take on cross-group project management and team organization.

Scientific self-image / professionalism:

- Document, version, evaluate and present project results
.

Students should work on a data science project on the basis of a sufficiently detailed description of the project objectives with any requirements already specified by the lecturer. The overall system to be developed must be modularized by the students accordingly, interfaces must be defined and, at the end, the interaction of the individual modules implemented must be tested by means of integration tests.

The sub-projects are worked on by individual groups consisting of 2-3 students. The students should also be responsible for project management and coordinate the implementation. For the implementation of the data science parts, the project should be based on established industry standards such as the CRISP-DM model.

In addition to the specified or selected implementation environments, a platform for versioning and tools for project planning and control are also used as part of the project implementation.

The tasks are based on current developments in the field of data science and may be defined and carried out in cooperation with partner companies of the UAS.

The project concludes with a public project demonstration.

Teaching methods

-   Project work with final presentation
-   Processing of project work in teamwork
-   Internship accompanying project work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Project work with final presentation and report - 100% of the overall grade

Requirements for the awarding of credit points

Successful project work and presentation

Applicability of the module (in other degree programs)

Bachelor's degree in computer science

Literature

Matzka, S. (2021): Crashkurs KI im Unternehmen. Haufe Lexware

Informatik und Gesellschaft
PF

2 SWS

2.5 ECTS

Number
45201
Language(s)
de
Duration (semester)
1
Contact time
30 h
Self-study
45 h

Learning outcomes/competences

After successfully completing module I&G, students have acquired professional competence because they ...

can describe the subject of computer science and its significance for society
distinguish and explain the concepts of ethics, morality, responsibility, value and dilemma.
be able to explain the main features of professional ethics in IT.

After successfully completing the I&G module, students will have acquired self-competence because they ...

are able to address their responsibility as computer scientists
they begin to deal with their own role as computer scientists

After successfully completing module I&G, students have acquired social competence because they ...

can recognize, describe and discuss the impact of IT on an individual and social level

After successfully completing module I&G, students have acquired professional field competence because they ...

can derive activities from their knowledge that they can place in project procedure models

Classification of the subject computer science & society

Socio-technical perspective, the importance of communication and its representation in technical systems

Concepts of the sociology of technology and work and organizational psychology

Ethics in computer science

Socio-technical design principles and process model for IT projects

Legal framework

Application of the ethical guidelines of the GI to current social issues related to IT

Teaching methods

Lecture in seminar style, with blackboard writing and projection

Group work

Seminar

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

project-related work with documentation and presentation followed by an oral examination

oral examinations

Homework

presentations

Requirements for the awarding of credit points

Module I&G is successfully completed without grading if a student ...

has actively participated in at least one discussion round on the application of the ethical guidelines of the GI and was able to answer questions from the lecturer on the content.
also created and presented a poster on a given topic in group work and discussed it with the auditorium
also actively participated in at least one full day of the seminar and was able to answer questions from the lecturer on the content.

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor of Medical Informatics

Literature

Gesellschaft für Informatik e.V. (2021): Ethischer Kompass für Informatik-Fachleute - Basierend auf den ethischen Leitlinien der Gesellschaft für Informatik. Gesellschaft für Informatik e.V. Online verfügbar unter https://gi.de/fileadmin/GI/Allgemein/PDF/GI_Ethischer_Kompass.pdf (abgerufen am 10. März 2025).

Kienle, Andrea; Kunau, Gabriele (2014): Informatik und Gesellschaft - eine sozio-technische Perspektive. München: Oldenbourg.

Loll, Anna Catherin (2017): Akteure im Bereich Informatik und Gesellschaft. In: Informatik Spektrum, 40, 4, S. 345-350.

Pretschner, Alexander; Zuber, Niina; Gogoll, Jan; Kacianka, Severin; Nida-Rümelin, Julian (2021): Ethik in der agilen Software-Entwicklung. In: Informatik Spektrum, 2021, 44, S. 348-354.

Webseite Gesellschft für Informatik

Webseite Netzpolitik.org

Webseite Humanetech

Webseite irights

Projektarbeit
PF

0 SWS

5 ECTS

Number
46193
Language(s)
de
Duration (semester)
1
Self-study
150 h

Learning outcomes/competences

Through the project work, students learn the following skills, which prepare them to write their final thesis later on and qualify them for their career entry:

Solving computer science-specific problems where possible in a business context by engineering a software/hardware solution (i.e. specifying requirements, weighing up and evaluating alternative solutions, modeling systems and ensuring quality) taking into account limited resources.
Conducting the work as a project (i.e. setting objectives and planning projects, pre- and post-calculation of the time required), and
Production of the written work using scientific working methods (including literature research, correct citation)
Evaluate the results of your own work
Ability to work in a team with developers and (where possible) users, in particular: to present work results, to lead and moderate meetings and to resolve conflicts.
Dealing with practically relevant tasks

For further details, see process description PB-PAAA (Annex IV).

The content of a project work is assessed according to effort and complexity, originality and independence, scientific working technique and methodical approach, practical implementation, style and external form.

Students have the right to suggest a project topic. The project should preferably be carried out outside the university (further details are regulated by the VA-PAAA-EXT procedural instructions). Group work is desired. The specific knowledge directly required in the projects will be taught in block courses if necessary. Regular project meetings give students the opportunity to acquire the above-mentioned teamwork skills by practicing them. In particular, quality assurance is trained through the presentation of results from analysis, design and implementation.

In general, project work 1 and 2 are completed as one project; in individual cases, they can be separated (see curriculum).

The total workload for project work 1 and 2 is 450 hours.

Teaching methods

Project work; final presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Project work with oral examination

Requirements for the awarding of credit points

Successful project work

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Literature

Muss von den Studierenden selbst in Bezug zum gewählten Thema der Projektarbeit ermittelt werden.

Übergreifend:

- Wissenschaftliches Arbeiten - Wissenschaft, Quellen, Artefakte, Organisation, Präsentation - Helmut Balzert, Christian Schäfer, Marion Schröder - W3L, 2. Aufl., 2011

Seminar (Inhalt)
PF

2 SWS

2.5 ECTS

Number
45182
Language(s)
de
Duration (semester)
1
Contact time
30 h
Self-study
45 h

Learning outcomes/competences

After successfully completing this module, students will be able to

Knowledge and Understanding

Name and understand the content-related competencies corresponding to the respective focus of the seminar. (Note: Since the content varies, the placeholder competency for the specific subject knowledge is set here).

Use, apply and generate knowledge

Use scientific methods to develop a presentation on the content focus.
Independently research and evaluate technical and scientific content.
structure and document information.
To write a scientific term paper.
Independently develop scientific texts.
Develop content relevant to the professional field.
Apply the skills they have learned in their studies and career.

Communication and cooperation

Working in groups and interacting within groups.
Create presentations and present results.
Present and defend content in groups.

Scientific self-image / professionalism

Structure scientific texts independently.

The seminars include topics that expand students' specialist academic skills. Students prepare a presentation on a selected special topic in business administration, computer science and/or business informatics and present the content. The topics are offered each semester with new, up-to-date content by all professors and lecturers and are offered to students in the university's electronic information service (web) (https://fh.do/inf/seminare). Examples of courses are Modern Supply Chain Management for Information Logistics, Business Simulation and Social Networks. The professional orientation of the seminars is strengthened by the use of lecturers from Business Studies with special qualifications in the subjects.

Teaching methods

Seminar

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Presentation

Requirements for the awarding of credit points

successful presentation

regular participation in at least 80% of the attendance dates

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor of Medical Informatics

Literature

Literatur muss vom Studierenden selbst ermittelt werden.

Übergreifend:

Balzert, H.; Schröder, M. und Schäfer, C.; Wissenschaftliches Arbeiten; W3l; Witten; 2. Aufl.; 2011

Componentware
WP

4 SWS

5 ECTS

Number
46808
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing this module, students will be able to:

knowledge and understanding
- precisely define the term component and distinguish it from other concepts.
- explain the challenges of distributed systems and typical problems in enterprise applications (such as transaction protection, security, access control, internationalization, scalability and availability).
- Summarize solution approaches with and without middleware, the concept of the application server and the EJB specification in your own words.
- Explain the difference between a specification and its concrete implementation using examples.

Use, apply and generate knowledge
- sketch and design distributed, component-based systems using UML.
- practically apply learned EJB skills (including Stateless/Stateful/Singleton Session Beans, Message Driven Beans, Timer Services and Entity Manager) on the glassfish application server.
- systematically develop and implement an independent software solution for any application domain as part of a project.

Communication and cooperation
- Work on problems of medium to high complexity within a team and solve them together responsibly.
- to develop an EJB solution cooperatively in a team, to document it professionally and to formulate solution approaches in discourse.

Scientific self-image / professionalism
- justify their own practical approach to project development with theoretical knowledge of software architecture principles.
- assess their own skills in solving technical problems (such as transaction management) and use their freedom of design and decision-making under guidance.

Introduction to component-based software development and application of what has been learned in practical examples based on EJB. After successful participation in the module courses, students will be able to:

General basics of component technology (motivation, definitions, goals,...)

Fundamental terms and challenges of enterprise applications (transaction protection, security, access control, internationalization, scalability, availability, ...)

Software architecture principles and concepts for defining software components and platforms

Concept of the application server

Stateless session beans

Stateful session beans

Singleton session beans

Message Driven Beans

Timer Services

Entity Manager and Persistent Entities

Transaction management

Characteristic features of component-based systems

Teaching methods

Lecture in seminar style, with blackboard and projection

Exercise to accompany the lecture

Solving practical exercises in individual or team work

Internship accompanying the lecture

project work accompanying the lecture with final presentation

Exercises or projects based on practical examples

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Project work with oral examination

Presentation

Semester-accompanying study achievements (bonus points)

Requirements for the awarding of credit points

passed oral examination (weighting: 50%)

successful project type and presentation (weighting: 50%)

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Literature

Oliver Ihns et. al.: EJB 3.1 professionell. Grundlagen- und Expertenwissen zu Enterprise JavaBeans 3.1 inkl. JPA 2.0, dpunkt.verlag GmbH, Auflage: 2., 2011

Jan Leßner, Werner Eberling: Enterprise JavaBeans 3.1: Das EJB-Praxisbuch für Ein- und Umsteiger, Carl Hanser Verlag GmbH & CO. KG; Auflage: 2, 2011

Clemens Szyperski, Dominik Gruntz and Stephan Murer: Component software. Beyond object-oriented computing, Pearson, 2nd Edition, 2002

CBSE-Proceedings: nth International Symposium on Component-Based Software Engineering

Adaptive Systeme
WP

4 SWS

5 ECTS

Number
46901
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the module courses, students are able to ...

Know and understand:

to recognize problems in which adaptive systems can be used to solve problems
to recognize that adaptive systems methods can be used to describe properties of technical but also business and social systems and to analyse their behaviour.
implement adaptive systems on the basis of the models explained and, if possible, evaluate them.
recognize the limits of adaptive systems.

Deploy, apply and generate knowledge:

Develop and analyze solutions to problems with adaptive systems.
Use computational intelligence methods for the design of adaptive systems

Basics and examples of adaptive and complex systems and their application (e.g. in the area of control systems, networks and the web)

Modeling of adaptation processes using various adaptive techniques

Theory and application of soft computing methods (e.g. evolutionary algorithms, particle swarm optimization, ant colony optimization, fuzzy logic, neural networks and modern machine learning methods) for system adaptation to (context) changes

Application of data classification methods to decision support systems (e.g. rating systems, collaborative and social recommendation systems)

Selected current methods from the field of computational intelligence and adaptive systems

Teaching methods

Lecture in seminar style, with blackboard writing and projection

Internship to accompany the lecture

Processing programming tasks on the computer in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of

100% of the overall grade consists of a written examination (90-120 minutes) or oral examination (20-30 minutes) (according to the current examination schedule), in which students analyze application scenarios, explain various theoretical principles and apply them situationally

Requirements for the awarding of credit points

passed written examination or passed oral examination (according to current examination schedule)

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Bachelor of Computer Science

Literature

J. Schmidt, Chr. Klüver, J. Klüver, Programmierung naturanaloger Verfahren, Vieweg+Teubner Verlag (2010)
R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, G. Ruß, M. Steinbrecher, Computational Intelligence, Zweite Auflage, Vieweg+Teubner Verlag (2015)
W.-M. Lippe, Soft-Computing, Springer Verlag (2005)
A. Kordon, Applying Computational Intelligence, Springer Verlag (2010)
I. Witten, E. Frank und M. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 4. Auflage, Morgan Kaufmann (2017), elektronische Version im Intranet verfügbar

Weitere aktuelle Literatur wird in der Vorlesung bekannt gegeben

Anerkannte Wahlpflichtprüfungsleistung
WP

4 SWS

5 ECTS

Number
46991
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students will be able to:

Knowledge and understanding:

describe the structure of quality models
name different heuristics for test case generation
describe the limitations of program tests
describe different integration strategies
Name typical sources of error
classify quality assurance measures

Use, application and generation of knowledge:

distinguish between analytical and constructive measures
select metrics to measure quality
organize quality assurance measures
execute manual test procedures
execute analytical test procedures
organize software maintenance

Communication and cooperation:

Specify non-functional requirements
Organize quality assurance processes

Scientific self-image / professionalism:

to achieve a defined level of quality
select and use suitable tools (constructive quality assurance)

Module description:
Teaching the knowledge required to achieve a defined level of quality in software development. The analytical and constructive measures for quality assurance are known and can be applied in a targeted manner. Methodical approach to software maintenance.

Module structure:

Quality models
Sources of error
Constructive measures
Manual inspection methods
Tools
Black box test
White box test
Metrics
Static code analysis
Test management
Automation (software infrastructure)
Maintenance and care

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving exercises in individual or team work

Solving practical tasks in the practical course in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written written examination (between 60 and 90 minutes)

Requirements for the awarding of credit points

Passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Literature

Balzert, H.; "Lehrbuch der Softwaretechnik, Softwaremanagement", Spektrum Akademischer Verlag, Heidelberg, 2008

Binder, R.V.; "Testing Object-Oriented Systems", Addison-Wesley, Boston, 2000

Hoffmann, D.W.; "Software-Qualität", Springer Vieweg, Berlin, 2013

Liggesmeyer, P.; "Software-Qualität", Spektrum Akademischer Verlag, Heidelberg, 2009

Ludewig, J.; Lichter, H.; "Software Engineering", dpunkt.verlag, Heidelberg, 2023

Spillner, A.; Linz, T.; "Basiswissen Softwaretest", dpunkt.verlag, Heidelberg, 2024

Sneed, H.M.; Seidl, R.; Baumgartner, M.; "Software in Zahlen", Hanser, München, 2010

Anerkannte Wahlpflichtprüfungsleistung
WP

0 SWS

5 ECTS

Number
46994
Duration (semester)
1

Anerkannte Wahlpflichtprüfungsleistung
WP

0 SWS

5 ECTS

Number
46999
Duration (semester)
1

Angewandte Logiken
WP

4 SWS

5 ECTS

Number
46817
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Technical and methodological competence:

After successful participation in the module courses, students are able to apply the basics of formal-logical modeling of dynamic processes as well as techniques of formal specification and verification of models.
After successfully completing the module courses, students will be able to
transfer advanced formal logic concepts of computer science - in particular concrete classical and non-classical logics, logic concepts and methodologies - to various problems in computer science, adapt them to the respective needs and finally apply them practically across disciplines.

Self-competence:

After successful participation in the module courses, students are able to independently deal with current research papers on formal logic modeling and verification in computer science and understand the core statements.

Social skills:

After successfully completing the module courses, students will be able to present didactically prepared formal logic topics and issues in presentations. In particular, they will be able to present complex formal-logical issues at different levels of granularity (from conveying the pure underlying idea to formulating the exact mathematical facts).

The event includes the following topics:

Classical concepts of modal logic (such as the modalities possibility and necessity) and their relevance in computer science
Syntax and semantics of classical modal and temporal logics (such as CTL*, CTL and LTL) and their applications
Formal-logical specification and modeling of computer science processes using possible-world semantics
(Automated) verification of modeled processes using model checking methods and their applications in practice
Syntax and semantics of epistemic logics (such as belief sets and epistemic modal logic) and their relevance for computer science
Exemplary application of the topics learned: depending on the interests and professional background, various example applications can be chosen such as Formal Hardware Verification , Modeling Dynamic Processes , Concurrency, etc.
Sensible intensional / propositional logics and their applications in modern computer science applications
Relevance of logics in the applications of artificial intelligence

Teaching methods

Lecture in seminar style with blackboard writing and projection

Exercise accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Presentation [scope: 50%] (45 minutes)

Written exam [scope: 50%] (60 minutes)

Requirements for the awarding of credit points

Passed presentation

Passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor of Medical Informatics

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Literature

Hughes und Cresswell A New Introduction To Modal Logic, Routledge Chapman & Hall,
Kropf Introduction to Formal Hardware Verification, Springer-Verlag Berlin and Heidelberg, 1999
Chagrov und Zakharyaschev Modal Logic, Oxford University Press, 1997
Gardenfors - Knowledge in Flux: Modeling the Dynamics of Epistemic States (Studies in Logic), College Publications, 2008
Bab - Epsilon_mu-Logik - Eine Theorie propositionaler Logiken, Shaker Verlag Aachen, 2007

Ausgewählte Aspekte der Data Science 3
WP

4 SWS

5 ECTS

Number
46115
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

The contents can be found in the published supplementary document for the specific event.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Lecture in seminar style, with blackboard and projection

exercise accompanying the lecture

Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written written examination

project-related work with documentation and presentation followed by an oral examination

oral examinations

Homework

Presentations

Requirements for the awarding of credit points

passed exam

Applicability of the module (in other degree programs)

Master's degree in Computer Science

Literature

siehe Zusatzdokument zur konkreten Veranstaltung

Ausgewählte Aspekte der Informatik 1 (Katalog Informatik gültig für Data Science)
WP

4 SWS

5 ECTS

Number
46125
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

This module combines courses on various computer science topics that are not offered regularly. The contents and competencies are published each semester in an additional document.
The competencies are derived from the published supplementary document for the specific course.

The contents can be found in the published supplementary document for the specific event.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Lecture in seminar style, with blackboard and projection

exercise accompanying the lecture

Internship accompanying the lecture

Forms of examination

Written written examination

project-related work with documentation and presentation followed by an oral examination

Oral examinations

Homework

presentations

Requirements for the awarding of credit points

Passed exam

Literature

siehe Zusatzdokument zur konkreten Veranstaltung

Ausgewählte Aspekte der Informatik 2 (Katalog Informatik gültig für Data Science)
WP

4 SWS

5 ECTS

Number
46126
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

The contents can be found in the published supplementary document for the specific event.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Lecture in seminar style, with blackboard and projection

exercise accompanying the lecture

Internship accompanying the lecture

Forms of examination

Written written examination

project-related work with documentation and presentation followed by an oral examination

Oral examinations

Homework

presentations

Requirements for the awarding of credit points

Passed exam

Literature

siehe Zusatzdokument zur konkreten Veranstaltung

Ausgewählte Aspekte der Informatik 3 (Katalog Informatik gültig für Data Science)
WP

4 SWS

5 ECTS

Number
46127
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Courses on various computer science topics that are not offered regularly are summarized in this module. The contents and competencies are published each semester in an additional document.
The competencies are derived from the published supplementary document for the specific course.

The contents can be found in the published supplementary document for the specific event.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Lecture in seminar style, with blackboard and projection

exercise accompanying the lecture

Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written written examination

project-related work with documentation and presentation followed by an oral examination

Oral examinations

Homework

presentations

Requirements for the awarding of credit points

passed exam

Literature

siehe Zusatzdokument zur konkreten Veranstaltung

Datenethik und Datenschutz
WP

4 SWS

5 ECTS

Number
46818
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the module courses and completion of the project work, students are able to ...

recognize and apply data ethics principles and assess them in complex contexts
to reflect on and classify data science methods in the context of ethical aspects
to be able to apply methods, concepts and legal principles of data protection in the context of information technology
Analyze and compare case studies of relevance to data ethics
Evaluate overall concepts for algorithms / software systems and data analysis

Introduction to data protection and data ethics

Definitions and basics.
Introduction to data ethics principles such as fairness, transparency and responsibility.
Historical context and examples from practice.

Data ethics basics

Principles of ethics (e.g. utilitarianism, deontology) in the data context.
Analysis of case studies, e.g. algorithmic bias in AI systems.

Data protection concepts and legal frameworks

Legal basics (introduction to the GDPR, AI Act and its main principles)
Identification and evaluation of processed personal data.
Privacy by Design and Privacy by Default.
Creation of data protection declarations.

Methods of data science and ethical reflection

Methods for the ethical design of data science projects, e.g. EDAP Ethical Deliberation in Agile Processes or Data Ethics Canvas
Case studies with relevance to data ethics (topics such as facial recognition, surveillance systems, healthcare).
Reflection on data protection problems and discrimination through algorithms, as well as possible solutions.

Technologies and system development

Encryption, anonymization and other technical data protection measures (possibilities and limits)
Use of international IT services in compliance with data protection regulations.
Development of algorithms and software systems in compliance with data protection and ethics.

Teaching methods

seminar-style teaching with flipchart, smartboard or projection

Internship accompanying project work

Project work in teamwork

Project work with final presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Seminar elaboration and presentation of a team

Project work with final presentation

Requirements for the awarding of credit points

successful project work

successful oral presentation

successful elaboration and presentation of a topic

Applicability of the module (in other degree programs)

Bachelor of Computer Science

Literature

• AI Act, https://artificialintelligenceact.eu/de/
• The Great Hack (Cambridge Analyticas großer Hack), 2019. Directed by Amer, K. and Noujaim, J.: Netflix.
• Datenethikkommission, Bundesministerium der Justiz und für Verbraucherschutz (2018) Empfehlungen der Datenethikkommission für die Strategie Künstliche Intelligenz der Bundesregierung.
• Thomas A. Degen, Jochen Deister, et al. (2021), IT- und Datenschutz-Compliance für Unternehmen: Leitlinien und Anwendungsfälle - Cloud, Social Media, Scrum, IoT, KI, Mobilitätsdaten: IT-Projekte und Leitlinien nach DSGVO
• Flick, C. (2016) 'Informed consent and the Facebook emotional manipulation study', Research Ethics, 12(1), pp. 14-28.
• Gesellschaft für Informatik e.V. 2018. Technische und rechtliche Betrachtungen algorithmischer Entscheidungsverfahren. In: Studien und Gutachten im Auftrag des Sachverständigenrats für Verbraucherfragen (ed.). Berlin: Sachverständigenrat für
Verbraucherfragen.
• o.V. (2021) Ethischer Kompass für Informatik-Fachleute - Basierend auf den ethischen Leitlinien der Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.
• Hochrangige Expertengruppe für KI (HEG-KI) (2019) Ethik-Leitlinien für eine vertrauenswürdige KI. Brüssel: Europäische
Kommission.
• Müller, L.-S. and Andersen, N. (2017) 'Denkimpuls Digitale Ethik: Warum wir uns mit Digitaler Ethik beschäftigen sollten – Ein Denkmuster', Initiative D21. http://initiatived21.de/app/uploads/2017/08/01-2_denkimpulse_ag-ethik_digitale-ethik-eindenkmuster_
final.pdf (Accessed 28. November 2021).
• A. Pretschner, N. Zuber, J. Gogoll, S. Kacianka and J. Nida-Rümelin(2021): Ethik in der agilen Software-Entwicklung in: Informatik Spektrum 2021 Vol. 2021 Issue 44 Pages 348-354
• Spiekermann, S. 2018. Kann man Ethik standardisieren? In: Köver, C. and Dachwitz, I. (eds.) Netzpolitik-Podcast Folge 161.
• Strecker, S. 2019. Maschinenethik - Gespräch mit Oliver Bendel. In: Strecker, S. (ed.) Perspektiven | Wirtschaftsinformatik- Podcast
• Europäische Union, Charta der Grundrechte der Europäischen Union, 2019
Europäische Union, Verordnung (EU) 2016/679 des Europäischen Parlaments und des Rates vom 27. April 2016 zum Schutz natürlicher
Personen bei der Verarbeitung personenbezogener Daten, zum freien Datenverkehr und zur Aufhebung der Richtlinie 95/46/E125

Digitale Bildverarbeitung
WP

4 SWS

5 ECTS

Number
46814
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

The course deals with the development and analysis of systems that use digital image processing methods. After successfully completing the course, students will have acquired the following skills:

Knowledge (knowledge):
- know the stages of digital image processing and can explain them
- know the most important mathematical and algorithmic concepts of digital image processing and can apply them in software codes
- know examples of the industrial application of digital image processing
- know the basics of machine learning methods including deep learning for image processing tasks

Skills
- can solve image processing problems by combining the methods covered in the course (building an image processing pipeline)
- can develop simple image processing applications using the programming system Matlab® or the programming language Java or Python and ImageJ
- can evaluate developed image processing pipelines
- can plan, implement and present image processing mini-projects together in a team

Competencies (personal and social skills)
- can formulate ideas and proposed solutions orally and in writing
- can solve tasks in exercises and practicals independently and present the results
- can develop solutions cooperatively in the exercise and project phases
- can cooperatively plan, distribute and jointly carry out tasks for solutions in the project phases
- can argue in discussions in a goal-oriented manner and deal with criticism objectively
- can present the results of group work together
- can evaluate project results and formulate suggestions for improvement
- can recognize and reduce misunderstandings between discussion partners

- Introduction to the programming language and environment Matlab®
- Overview of image processing hardware and software
- Image acquisition and discretization
- Methods for image restoration, image enhancement and geometric manipulation of images
- Point operations, linear and non-linear filters
- Morphological image processing and the processing of color images
- Discrete Fourier transform (1D and 2D) and applications and limits
- Methods for image segmentation, feature extraction and image analysis
- Pattern recognition and image classification
- Modern image features
- Deep learning methods for image classification and object detection

Teaching methods

Solving practical exercises in individual or team work

Processing programming tasks on the computer in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written examination paper or oral examination (according to the current examination schedule)

Requirements for the awarding of credit points

passed written examination or passed oral examination (according to current examination schedule)

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor of Computer Science Dual

Bachelor of Computer Science

Literature

- H. Bässmann, J. Kreyss: Bildverarbeitung AdOculos, Springer-Verlag, 2004
- W. Burger, M. J. Burge: Digital Image Processing, Dritte Auflage, Springer-Verlag, 2015, elektronische Version im Intranet verfügbar
- A. Nischwitz, M. Fischer, P. Haberäcker: Computergrafik und Bildverarbeitung, Vieweg+Teubner Verlag, 2007
- R. C. Gonzalez, S. L. Eddins, R. E. Woods, Digital Image Processing, Vierte Auflage, Pearson, 2018
- R. C. Gonzalez, S. L. Eddins, R. E. Woods, Digital Image Processing Using MATLAB, Prentice Hall, 2004

ERP 1 (Standardsoftware)
WP

4 SWS

5 ECTS

Number
46828
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students will be able to:

Classify and differentiate between individual, standard and industry software.
Name the advantages and disadvantages of standard software.
evaluate the current market situation.
Name criteria for the selection of standard software.
apply a systematic approach to the selection of standard software.
Be familiar with procedure models for the introduction of standard software.
distinguish between different customization options for standard software and evaluate their respective consequences.
to gain an overview of the complexity of business processes in integrated systems.
design and implement functional enhancements to standard software.
understand and apply the importance of communication, conflict and team skills in implementation and customization projects.
to recognize and understand social problems of an ERP implementation and to deal sensitively with their consequences.
understand the requirements of different job profiles in the ERP environment (in particular sales, consulting, project management, application development)

General basics (definition of terms, historical development, ... )

Standardization concept (classification and differentiation from in-house development, degree of coverage, ... )

Integration aspects (technical and organizational integration, examples and consequences, ... )

Business management components (financial accounting, HR, logistics, production, ... )

selection process (market overview and breakdown, selection criteria, decision-making process, ... )

Introduction of an ERP system (project approach, implementation strategies, procedures)

Technical basics (system structure, hardware platforms and supported databases, ... )

Installation, maintenance and operation of an ERP solution

Adaptations to standard software (types of adaptations, their delimitation and consequences, ... )

Integrated development environments and programming languages

Inhouse developments (functional expansion of an ERP system in practical exercises using a mini-project)

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving practical exercises in individual or team work

Processing programming tasks on the computer in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of a written exam in which students are asked to recall basic knowledge from the lecture and remember the knowledge, in particular technical terms. Review questions on the respective chapters serve as preparation. In addition, they should be able to apply this knowledge to specific questions from practice and explain it if necessary.
Duration: 90 minutes

As optional coursework (bonus points) during the semester, a practice-oriented case study must be completed and a small extension developed under supervision. The practical knowledge and skills can then be deepened independently in a further (mini) project and applied as a transfer achievement.

Requirements for the awarding of credit points

passed written exam (at least 50% of the maximum achievable points)

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Bachelor of Computer Science Dual

Bachelor of Computer Science

Literature

Skript zur Vorlesung (Hesseler, M.)
Hesseler, M.; Görtz, M.; Basiswissen ERP-Systeme ; w3l-Verlag; Bochum; 2007;

Ergänzende Literaturempfehlungen (nicht zwingend erforderlich):
- Allweyer, T.; Geschäftsprozessmanagement ; w3l-Verlag; Bochum; 2005;
- Hesseler, M. und Rösel, C.; ERP-Übungsbuch: Entwicklung einer einfachen Fuhrpakrverwaltung in Microsoft Dynamics NAV ; Books on Demand; Norderstedt; 2010;
- Hesseler, M. und Görtz, M.; ERP-Systeme im Einsatz ; w3l-Verlag; Herdecke; 2009;

Effiziente Algorithmen und Datenstrukturen
WP

4 SWS

5 ECTS

Number
46889
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Technical and methodological competence:

Be able to describe basic algorithmic methods
Be able to assess problems in terms of their modeling possibilities and algorithmic complexity.
Be able to describe and implement efficient algorithms and data structures for selected basic problems.

Basics

O-notation
Graphs

Graph algorithms

Shortest paths
Minimal spanning trees
Flows in networks
Matchings
Tours

Algorithmic techniques

Divide and Conquer
Dynamic programming
Greedy algorithms

Optimization problems

Backtracking
Branch-and-bound
Approximation algorithms

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Exercise accompanying the lecture

Solving practical exercises in individual or team work

Group work

Individual work

Presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor of Computer Science

Bachelor of Computer Science Dual

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics with practical semester

Bachelor of Medical Informatics Dual

Literature

T. Cormen, C. Leiserson, R. Rivest, C. Stein: "Algorithmen - Eine Einführung", Oldenbourg, 4. Auflage, 2013

T. Ottmann, P. Widmayer: "Algorithmen und "Datenstrukturen", Spektrum Akademischer Verlag, 6. Auflage, 2017

G. Pomberger, H. Dobler: "Algorithmen und Datenstrukturen", Pearson Studium, 2008

R. Sedgewick, K. Wayne: "Algorithmen", Pearson Studium, 2014

R. Wanka: "Approximationsalgorithmen - Eine Einführung", Teubner, 2006

B. Vöcking, H. Alt, M. Dietzfelbinger, R. Reischuk, C. Scheideler, H. Vollmer, D. Wagner: "Taschenbuch der Algorithmen", Springer, 2008

Fortgeschrittene Informationssicherheit
WP

4 SWS

5 ECTS

Number
46900
Language(s)
en
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

The students are able to apply methods,
best practices and - apply methods, best practices and software tools relevant in practice for the development of secure software.
- independently evaluate various cryptographic methods as part of a software development project and select adequate cryptographic methods on this basis.
- independently develop software that uses cryptographic methods and systematically test the software.

- Java Cryptography Architecture and API
- Legion of the Bouncy Castle Java Cryptography APIs
- Block ciphers: AES, padding, block modes, use as stream ciphers
- Stream ciphers: ChaCha20, generation of key streams
- Password-based encryption/decryption
- Key management
- Message digests, MACs, key derivation functions
- Asymmetric cryptography: DH, RSA, DSS, ECDSA
- Methods for developing secure software: e.g.
- Design principles according to Saltzer and Schroeder
- Secure coding guidelines (Java)
- Unit testing when using cryptography
- Penetration testing with software tools
- Best practices (OWASP Top 10, SAMM, ASVS)

The language of instruction is English.

C can be used as an alternative to Java.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Project work accompanying the lecture with final presentation

Individual work

Inverted teaching (inverted classroom)

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Project-related work (100%)

Requirements for the awarding of credit points

Successful project work

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

- D. Hook und J. Eaves: Java Cryptography: Tools and Techniques, Leanpub, 2023
- F. Long, D. Mohindra, R. C. Seacord, D. F. Sutherland und D. Svoboda: Java Coding Guidelines: 75 Recommendations for Reliable and Secure Programs, Addison-Wesley Professional, 2013
- K. Schmeh: Kryptografie Verfahren - Protokolle - Infrastrukturen, 6. Auflage, dpunkt.verlag, 2016
- R. E. Smith: A Contemporary Look at Saltzer and Schroeder s 1975 Design Principles, IEEE Security & Privacy, 10(6), 20-25, 2012

IT-Servicemanagement
WP

4 SWS

5 ECTS

Number
46905
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Transfer of basic knowledge regarding the importance and use of IT service management in the company. Theoretical knowledge of the five phases and their processes, roles and functions of the IT Infrastructure Library (ITIL) lifecycle model. Consolidation and practical application of previously acquired specialist knowledge using practical examples.

Technical and methodological competence:

Distinguishing between IT management and IT service management
Naming the reasons for and objectives of using ITIL
Differentiating the different phases of the ITIL lifecycle
Use case studies to deepen the knowledge gained and develop your own solutions in the ITIL environment
Design and implement your own ITIL implementation scenarios in exemplary case studies
Develop detailed processes based on the ITIL phases for specific practical tasks

Interdisciplinary methodological competence:

Selecting suitable communication structures for service and support processes/structures
Systematic prioritization of activities and projects
Knowing error cultures (human factor in stressful situations)
Evaluating classic conflicts between design and operational functions
Classification of DevOps and agile development in ITIL phases
Systematic use of IT KPIs to measure the achievement of objectives

Professional field orientation:

Knowledge of the requirements of different job profiles in the IT service management environment (service owner, service manager, process owner, process manager, etc.)
Applying IT processes in the context of IT service management
Knowing roles and responsibilities within IT service management
Selecting and using suitable models, concepts and tools

IT Management and Business Service Management (BSM) Basics

Business Process Modeling Notation Basics

IT service management (ITSM) basics

Concepts and methods of IT service management

ITIL basics and history

ITIL (IT Infrastructure Library) V3 2011

Service strategy (Service Strategy)

Service design (Service Design)

Service Transition (Service Transition)

Service Operation (Service Operation)

Continuous Service Improvement

Teaching methods

Lecture in seminar style, with blackboard writing and projection

Solving practical exercises in individual or team work

Case studies

Role-playing games

Exercises or projects based on practical examples

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Examinations during the semester (scope 1/3) + oral examination (scope 2/3)

Requirements for the awarding of credit points

Semester examinations and oral examinations must be passed in total.

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

WXYZ

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Literature

Axelos, ITIL® Service Continual Service Improvement; Edition2011; London TSO; 2013

Axelos, ITIL® Service Design, Edition 2011; London TSO; 2013

Axelos, ITIL® Service Operation; Edition 2011; London TSO; 2013

Axelos, ITIL® Service Strategy; Edition 2011; London TSO; 2013

Axelos, ITIL® Service Transition; Edition 2011; London TSO; 2013

Beims, M.; IT-Service Management mit ITIL®, ITIL® Edition 2011, ISO 20000:2011 und PRINCE2® in der Praxis; 3. Auflage; Dr. Carl Hanser Verlag; 2012

Buchsein, R., Victor, F. Günther, H., Machmeier, V.; IT-Management mit ITIL® V3: Strategien, Kennzahlen, Umsetzung; 2. Auflage; Vieweg; Wiesbaden; 2008

Olbrich, Al.; ITIL kompakt und verständlich; 4. Auflage; Vieweg; Wiesbaden; 2006

Victor, F., Günther, H.; Optimiertes IT-Management mit ITIL; 2. Auflage; Vieweg; Wiesbaden; 2005

Zarnekow, R., Fröschle, H.-P.; Wertorientiertes IT-Servicemanagement: HMD - Praxis der Wirtschaftsinformatik (Heft 264); dpunkt Verlag; Heidelberg; 2008.

Informations- und Business Performance Management
WP

4 SWS

5 ECTS

Number
46909
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Knowledge and understanding competence (professional competence)
The students
- know and understand central business and analytical concepts such as strategic alignment, document management, balanced scorecard, key performance indicator systems and predictive modeling and can classify their significance for analytical information systems,
- recognize and explain the core concepts of the information supply chain, multidimensional modeling and the technical architectures of MOLAP, ROLAP and in-memory systems,
- have a basic understanding of the concepts of data warehousing, data mining and big data processing,
- understand advanced business management methods such as planning and budgeting and can explain their requirements for analytical IT systems,
- know lifecycle models, reference models and modeling languages in the context of analytical applications and can classify them systematically,
- can name and distinguish information architectures and evaluate them with regard to their areas of application.
Skills (methodological and application competence)
The students are able to
- derive requirements from business methods and translate them into technical concepts of analytical applications,
- develop and analyze multidimensional models and prepare them for reporting and analysis purposes,
- Build reports, dashboards and analysis models from raw data and define suitable KPI structures,
- Select lifecycle models such as Kimball, Inmon or CRISP-DM and apply them to a specific business intelligence project,
- design and implement a small BI system in a team and evaluate it in terms of data quality, modeling and analytical benefits,
- compare technical and conceptual alternatives of analytical architectures and make a well-founded selection.
Social competence
The students
- work constructively, coordinated and goal-oriented in project teams,
- communicate analysis results to the right audience and actively contribute to joint problem solving,
- take responsibility for subtasks and support collaborative work processes as part of the semester-long project.
Self-competence
The students
- reflect on their modeling, analysis and architecture decisions and can justify them professionally,
- develop an awareness of the importance of data quality, transparency and traceability in analytical systems,
- organize their work along lifecycle models and apply basic principles of project management independently.

Overview and introduction
Chapter I
- Information and decision theory
- Information supply chain
- Business signals
- Operational and analytical applications
- Balanced scorecard
Chapter II
- Accounting, controlling, strategic planning
- Extraction, transformation, loading (ETL)
- Concept of the data warehouse
- Multidimensional modeling
Chapter III
- Predictive analytics, data mining methods and applications
Chapter IV
- Big data and document management
Chapter V
- Multidimensional business applications
- OLAP analysis
- Business planning
- Group consolidation
Chapter VI
- Case studies of analytical applications
Chapter VII
- Strategic Business and IT Alignment
- Lifecycle models for information management projects

Semester-accompanying group project:
Development of a reporting system for standard and OLAP reports based on tourism market research data using Microsoft SQL Business Intelligence Studio with the following sub-steps:

Understanding the question
Understanding the data
Processing the data
Modeling
Validation
Application

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Exercise accompanying the lecture

Solving practical exercises in individual or team work

Internship accompanying the lecture

Group work

Concluding presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

graded written examination 75% - 60min
graded semester-accompanying coursework 25% - compulsory attendance (two absences are tolerated, otherwise the semester-accompanying coursework will be reduced proportionately on the basis of the attendance dates) Group project (target number of three participants per group) over 8 weeks of 90min + acceptance interview 20min

Requirements for the awarding of credit points

passed written exam

successful acceptance interview

Applicability of the module (in other degree programs)

Bachelor of Business Informatics (compulsory subject 5th semester)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Bachelor of Computer Science

Literature

Bashiri, I., Engels, C., Heinzelmann, M., Strategic Alignment, Springer, 2010.

Cameron, S., SQL Server 2008 Analysis Services Step by Step, Microsoft Press, 2009, ISBN-10: 0-7356-2620-0.

CRISP-DM, 1.0 step-by-step data mining guide, CRISP-DM consortium, 1999, (abgerufen am 25.11.2010) http://www.crisp-dm.org/download.htm.

Engels, C., Basiswissen Business Intelligence, W3L Verlag, Witten 2009.

Heinrich, Lutz J.: Informationsmanagement. Seit 1985 im Oldenbourg Wissenschaftsverlag, München / Wien, 8. Aufl. 2005, 9. Aufl. 2009 (1. bis 3. und ab 8. Aufl. mit Ko-Autor), ISBN 3-486-57772-7.

Jiawei Han, M.Kamber, Data Mining: Concepts and Techniques, http://www.cs.sfu.ca/~han/bk/.

Robert S. Kaplan, David P. Norton: Balanced Scorecard. Strategien erfolgreich umsetzen. Stuttgart 1997, ISBN 3-7910-1203-7.

Kemper et.al., Business Intelligence, Vieweg, 3. Auflage, 2010, ISBN 978-3-8348-0719-9.

Kimball, R. et. al., The Kimball Group Reader, Wiley, 2010.

Kimball, R., Caserta J., The Data Warehouse ETL Toolkit, Wiley, 2004.

Krcmar, H.: Informationsmanagement. 6. Auflage, Springer, Berlin et al., 2015, ISBN 978-3-662-45862-4

Misner, S., SQL Server 2008 Reporting Services Step by Step, Microsoft Press, 2009, ISBN-10: 0-7356-2647-2.

Mitchell, T., Machine Learning, McGraw Hill, 1997.

Scheuch, R., Gansor, T., Ziller, C: Master Data Management: Strategie, Organisation, Architektur, dpunkt.verlag, 2012.

Plattner, H., Zeier, A.: In-Memory Data Management: An Inflection Point for Enterprise Applications, Springer, Berlin, 2011.

Informationssysteme im Gesundheitswesen
WP

4 SWS

5 ECTS

Number
44441
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Subject and methodological competence:
After completing the module, students will be able to name the most important information systems in the healthcare sector, how they work and their special features. Furthermore, students will be able to evaluate the various information systems with regard to their area of application and point out the advantages and disadvantages of the various solutions. In addition, students are able to independently develop solution and system concepts for given practical application scenarios.
Among other things, students are able to

describe the structure of an electronic patient record and explain the differences and areas of application of ePA, eFA, eGA
the students know the basics of medical information systems and can apply them to specific examples
name the modules of hospital information systems and practice management systems and assign the essential supported processes
to parameterize
name the structure and areas of application of HL7, DICOM and IHE
name the structure of the telematics infrastructure (TI) and TI applications, explain how they work and differentiate between the individual solutions
to reproduce the essential legal framework
to transfer knowledge about the functionality of available eHealth applications to specific use cases in order to develop solutions for supporting medical processes in the healthcare sector

Social competence:

They know the essential soft factors when using IT in healthcare

Professional field orientation:

They are familiar with the major providers of hospital information systems and their use
they know what types of information systems are available on the market
they know the common communication standards and terminology in the professional field of medical informatics
they know the basic solution approaches for essential support requirements in the healthcare sector and can transfer these to comparable scenarios

Basics of medical information systems

Structure and concepts of electronic patient records and other record systems

Modules and supported core processes of a hospital information system

Functions and supported core processes of a medical practice system

Basic communication standards in healthcare such as HL7 FHIR, DICOM, IHE, openEHR (syntactic interoperability)

Fundamentals of basic terminologies such as ICD, OPS, SNOMED-CT (semantic interoperability)

Legal framework (KHZG, E-Health Act, DVG, ...)

Development of the telematics infrastructure and applications on it (DiGAs, teleconsultations, KIM and others)

Example applications of eHealth: eGK, ePrescription, eMedication, health portal, telemedicine, eDocumentation

Parameterization of a hospital information system

Teaching methods

Seminar-style lecture, with blackboard and projection

Processing exercises during the lecture, possibly on the computer in individual or team work

Active, self-directed learning through the use of electronic learning materials

Excursion

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam, 60 - 90 minutes

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Literature

Baas, J. (2020). Digitale Gesundheit in Europa: menschlich, vernetzt, nachhaltig. Medizinisch Wissenschaftliche Verlagsgesellschaft.

Bachmann, W. (2009). Praxishandbuch IT im Gesundheitswesen: Erfolgreich einführen, entwickeln, anwenden und betreiben. Hanser Verlag.

Dugas, M. (2017). Medizininformatik. Springer Berlin Heidelberg.

Haas, P.: Medizinische Informationssysteme und Elektronische Krankenakten, Springer 2004.

Jehle, R., Czeschik, J. C., Freund, T., & Wellnhofer, E. (Eds.). (2015). Medizinische informatik kompakt: Ein Kompendium für mediziner, informatiker, qualitätsmanager und epidemiologen. Walter de Gruyter GmbH & Co KG.

Johner, C., Hölzer-Klüpfel, M., & Wittorf, S. (2020). Basiswissen medizinische Software: Aus-und Weiterbildung zum certified professional for medical software. dpunkt. verlag.

Leiner, F. (2012). Medizinische Dokumentation: Grundlagen einer qualitätsgesicherten integrierten Krankenversorgung; Lehrbuch und Leitfaden; mit 24 Tabellen. Schattauer Verlag.

Marx, G. (2021). Telemedizin: Grundlagen und praktische Anwendung in stationären und ambulanten Einrichtungen. Springer.

Simon, M. (2021). Das Gesundheitssystem in Deutschland: Eine Einführung in Struktur und Funktionsweise. Hogrefe AG.

Krankenhausinformationssystem M-KIS der Meierhofer AG (steht im Labor zur Verfügung) mit entsprechenden Handbüchern

Kooperative Systeme
WP

4 SWS

5 ECTS

Number
46912
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Knowledge and understanding

Students know the basics of social groups and essential categorizations of support by technical systems
Students understand the importance and effects of IT support for groups and communities

Use, application and generation of knowledge

The students are able to select, adapt and introduce concrete systems for studying on site and working in groups by comparing and analyzing
The students design cooperative systems based on the categories, technologies and design principles covered
The students apply learned concepts of group work in an interdisciplinary manner

Communication and cooperation

The students work on a term paper and presentation as group work and thus practise their social skills
The students examine and evaluate concrete cooperative systems in changing social constellations in work assignments in the seminar part
The students apply the concepts learned in this course on the topic of groups and the group support tools discussed

Scientific self-image / professionalism

The students assess the significance of cooperative systems for the IT landscape of organizations, companies and communities

Basic concepts of cooperative systems

Basic concepts of distributed systems

Concurrency control & synchronization

Awareness and design of multi-user interfaces

Project work

Community support and social networks

Knowledge management in groups & organizations

Teaching methods

seminar-style lecture with presentations, small group work and assignments

Participation requirements

Admission requirements for the examination: 60 ECTS credit points from examinations in
 semesters 1 and 2.

Forms of examination

Homework and

Presentation

oral examination

Requirements for the awarding of credit points

successful term paper and

successful presentation

passed oral examination

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Literature

Borghoff, U.M.; Schlichter, J.H. (1998): Rechnergestützte Gruppenarbeit - eine
Einführung in verteilte Anwendungen. Springer, 2., vollst. überarb. und erw. Aufl.

Gross, T.; Koch, M. (2007): Computer Supported Cooperative Work. München: Oldenbourg.

Haake, J. M.; Schwabe, G.; Wessner, M. (Hrsg.) (2012): CSCL-Kompendium. München: Oldenbourg Verlag, 2. Auflage.

Schwabe, G.; Streitz, N.; Unland, R. (2001): CSCW-Kompendium: Lehr- und Handbuch Zum Computerunterstützten Kooperativen Arbeiten.Heidelberg: Springer.

Mobile App Engineering
WP

4 SWS

5 ECTS

Number
46847
Language(s)
en
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students will be able to ...

understand the software-technical challenges in the development of mobile apps,
create and present relevant mobile app-specific development and results documents,

processes, activities, methods, techniques, languages and tools

to use for a mobile app-specific requirements analysis
to be used for the conception and design of mobile apps,
to be used for the implementation of mobile apps,
to be used for testing mobile apps,
to be used for the commissioning of mobile apps,

to be able to apply the roles and responsibilities in the field of mobile app engineering

The aim and content of the course is to teach suitable methods, techniques, languages and tools to professionally conceptualize, design, develop, test and commission mobile apps from a software engineering perspective. The entire life cycle of a mobile app is considered, including:

User-oriented collection and specification of the functional and non-functional requirements for a mobile app
GUI prototyping with low- and high-fidelity prototypes
UX/UI design,
Specification of the interaction design
Specification and the individual screen pages,
Implementation of mobile apps,
Testing mobile apps
Processes and activities for the go-live of a mobile app

The phases and activities to be carried out are described and illustrated in a practical way using suitable methods, techniques, languages and tools based on a large industrial mobile app development project.

In the practical part of the course - from the results and presentations of which the performance assessment is also derived - selected requirements, conception, design, development and test activities are carried out in project teams of four in order to develop a mobile app independently and autonomously. The students then present the results they have developed in the practical part in a 20-minute presentation on two dates.

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Internship accompanying the lecture

Processing programming tasks on the computer in individual or team work

Presentation of the results by the student project groups

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of two partial examinations:

By the middle of the lecture period, the following result documents on the conception and design of a mobile app are created and developed in project groups of four students and presented in a 20-minute presentation by 2 students; 50 (out of a total of 100) points can be achieved:
- UML use case diagram
- 4 personas & 4 scenarios
- Product backlog with all user stories
- UML class diagram
- UML state diagram of the click flow of the mobile app
- Static HiFi GUI prototype
In the last week of lectures, the other two students from the 4-person project group present the results of the design, implementation and testing of the mobile app with a further 50 (out of a total of 100) achievable points:
- Specification of the individual GUI pages
- Test plan, test protocols and results
- Runnable mobile app

The overall grade is then calculated by adding up the points achieved: a "sufficient (4.0)" can be achieved from 50 points and a "very good (1.0)" from 95 points.

Requirements for the awarding of credit points

If at least 50 total points are achieved, a grade of 4.0 (sufficient) is awarded and the teaching module is successfully passed so that the required credit points are awarded.

Applicability of the module (in other degree programs)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Literature

Vollmer, G. (2017): Mobile App Engineering, Heidelberg: dpunkt-Verlag.

Moderne Datenbanken
WP

4 SWS

5 ECTS

Number
46892
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the module courses and completion of the course-related project, students are able to design, implement and evaluate database applications and distributed database architectures with NoSQL databases. Expert knowledge:

Know and use NoSQL database models and demonstrate possible applications
Know and explain materialized and virtual information integration.
Evaluate and explain distributed database architectures for big data applications.

Social competence:

Developing, communicating and presenting non-relational database applications in small groups
Collaboratively create and compare non-relational database applications with relational solutions.
Critically evaluate solutions from others and provide constructive feedback.

Professional field orientation:

Know the requirements of different job profiles in the database environment (database administrator. Database developer, application developer, data protection officer)

Distributed databases and big data applications
Architectures for distributed database applications
Requirements and selection of databases (CAP theorem)
NoSQL databases, multi-model and NewSQL databases
Polyglot persistence
Selected algorithms (e.g. map-reduce algorithm)
Current applications

Teaching methods

Seminar-style teaching with flipchart, smartboard or projection

Processing programming tasks on the computer in individual or team work

Project work accompanying the lecture with final presentation

Group work

Active, self-directed learning through internet-supported tasks, sample solutions and accompanying materials

Homework to accompany the course

The lecture is offered as a video

Inverted teaching (inverted classroom)

Concluding presentation

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of a written exam (60 minutes) and coursework during the semester.
In the written exam, students should demonstrate basic knowledge of theoretical concepts, database architectures, database models and justify the selection of databases for given application scenarios.
Through coursework during the semester, students should design a self-selected application scenario in small groups and evaluate, present and reflect on the implementation with various NoSQL databases.

Requirements for the awarding of credit points

The performances are graded and must be passed with a total grade of 4.0. The performances consist of:

passed written examination (60%-100%) and
successful presentation (0%-40%) or successful mini-project (project-related work) (0%-40%)

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Bachelor of Computer Science

Literature

S. Edlich, A. Friedland, J. Hampe, B. Brauer, NoSQL Einstieg in die Welt nichtrelationaler Web 2.0 Datenbanken, Hanser Verlag 2010

M. Kleppmann, Designing data-intensive applications, O'Reilly Media (2017)

A. Bifet, Machine learning for data stream, MIT-Press (2017)

B. Ellis, Real-time analytics, Wiley & Sons (2014)

Aktuelle Fachliteratur

Numerische Algorithmen
WP

4 SWS

5 ECTS

Number
46840
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successful participation in the module courses and completion of the project work, students are able to ...

Calculate numerical representations
analyze numerical errors
Calculate fixed points, zeros and roots numerically
Calculate derivatives and integrals numerically
to solve linear systems of equations numerically
Solve eigenvalue and eigenvector problems numerically
Calculate numerically approximating and interpolating polynomials and splines

- Numerical representations and error analysis
- LR decomposition
- QR decomposition (Givens and Householder)
- Cholesky decomposition
- Banach's fixed point theorem
- Newton's method
- Heron method
- Secant method
- Descent method
- Divided-difference method
- Trapezoidal and Simpson's rule
- Norms and consequences in multidimensional
- Total step, single step and SOR methods
- Von Mises-Geiringer method
- Polynomial interpolation and approximation
- Spline interpolation and approximation
- Bilinear interpolation
- transfinite interpolation function

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving practical exercises in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor of Computer Science

Bachelor of Medical Informatics

Literature

• B. Lenze, Basiswissen Angewandte Mathematik - Numerik, Grafik, Kryptik. Eine Einführung mit Aufgaben, Lösungen, Selbsttests und interaktivem Online-Tool. Springer Vieweg Wiesbaden, 2020, zweite Auflage
• G, Bärwolf, Numerik für Ingenieure, Physiker und Informatiker, Springer-Verlag, Berlin-Heidelberg-New York, 2017, dritte Auflage
• G. Farin, Curves and Surfaces for CAGD, Academic Press, San Diego, 2002, fünfte Auflage.
• M. Hermann, Numerische Mathematik, de Gruyter-Oldenbourg, 2011, dritte Auflage
• T. Huckle, S. Schneider, Numerik für Informatiker, Springer-Verlag, Berlin-Heidelberg-New York, 2006, zweite Auflage.
• H. Prautzsch, W. Boehm, M. Paluszny, Bezier and B-Spline Techniques, Springer-Verlag, Berlin-Heidelberg-New York, 2010, erster Nachdruck.
• R. Schaback, H. Wendland, Numerische Mathematik, Springer-Verlag, Berlin-Heidelberg-New York, 2005, fünfte Auflage.
• J. Werner, Numerische Mathematik 1 und 2, Vieweg, Wiesbaden, 1992

Robotik
WP

4 SWS

5 ECTS

Number
46855
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Technical and methodological competence:

After completing the lecture, students will be able to

Understand and apply methods and concepts of robotics
design and implement stationary and mobile robotics applications
set up kinematic equations for mobile and stationary robots
select components for robotics applications
configure and program mobile and stationary robots

Objectives and areas of application of robotics

Design of stationary and mobile robots

Kinematics of stationary robots

Applications of stationary robots

Subsystems of robots (joints, drives, actuators and sensors)

Kinematics of mobile wheel-driven robots

Self-localization and navigation of mobile robots

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Exercise accompanying the lecture

Solving practical exercises in individual or team work

Internship accompanying the lecture

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

written exam paper

Requirements for the awarding of credit points

passed written exam

Applicability of the module (in other degree programs)

Bachelor of Computer Science

Literature

Corke, Peter: Robotics, Vision and Control: Fundamental Algorithms in MATLAB, second edition, Springer, 2017

Weber, Wolfgang: Industrieroboter: Methoden der Steuerung und Regelung, Carl Hanser Verlag, 3. Auflage, 2017

Siegwart, Roland; Nourbakhsh, Illah R.: Introduction to Autonomous Mobile Robots, MIT Press, 2nd Edition, 2011

Hesse, Stefan; Malisa, Viktorio (Hrsg.): Taschenbuch Robotik - Montage - Handhabung, Carl Hanser Verlag, 2010

Hertzberg, Joachim; Lingemann, Kai; Nüchter, Andreas: Mobile Roboter - Eine Einführung aus Sicht der Informatik, Springer Vieweg Verlag, 2012

Softwaretechnik C (Softwaremanagement)
WP

4 SWS

5 ECTS

Number
45261
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Knowledge and understanding:
After successful participation in the module courses, students will be able to

Presenting the goals, methods and relevance of software management
Determine suitable procedure and process models in the context of a software project
Describe the required methodology as well as the distribution of tasks and roles of the communicated procedure and process models
Name the methods, processes and activities of product management for software
Determine methods, processes and activities of process improvement as well as appropriate process improvement frameworks with respect to a business context

Use, application and generation of knowledge:
After successfully completing the module courses, students will be able to

classify software projects with regard to their framework conditions and requirements and assess their complexity
apply methods for eliciting (software) requirements
Organize the documentation and management of (software) requirements, especially in the context of agile procedure and process models
Organize risk management methods, processes and activities in software projects
Organize methods, processes and activities for planning and controlling software projects
Organize methods, processes and activities of quality management in software projects
Name methods, processes and activities of configuration management in software projects and organize methods, processes and activities of release management in software projects

Communication and cooperation:
After successfully completing the module courses, students will be able to

to independently structure core elements of software management and activities for coordination and communication in the creation of work results in small groups

Scientific self-image / professionalism:
After successful participation in the module courses, students will be able to
Classify software projects with regard to their framework conditions and requirements and structure them using software management methods

Introduction to software management: General overview of relevance as well as concepts and methods of software management

Procedure and process models in software engineering: Classification and methodology of relevant procedure and process models in software engineering, including the waterfall model, V-Model XT, Kanban, Scrum and SAFe, as well as the values and principles of the Agile Manifesto

Requirements management: classification and methodology of common processes, activities and concepts of requirements management, including elicitation techniques, administration and documentation in an agile context

Risk management: Classification and methodology of common processes, activities and concepts of risk management in a classic and agile context

Project management: Classification and methodology of processes, activities and concepts in the areas of planning and control of project management in a classic and agile context

Quality management: Classification and methodology of common processes, activities and concepts of quality management in a classic and agile context

Configuration management: Classification and methodology of common processes, activities and concepts of configuration management

Product Management: Classification and Methodology of Common Processes, Activities and Concepts of Product Management

Release management: Classification and methodology of common processes, activities and concepts of release management in a classic and agile context

Process improvement: Classification and methodology of common processes, activities and goals of process improvement, especially for maturity-based and agile approaches

Common framework models of process improvement

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Internship accompanying the lecture with group work and practical exercises

Solving practical exercises in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of a written exam in which students explain the methods and concepts of software management taught and use their knowledge to analyze practical case studies.
In the context of semester-long coursework (bonus points) with a final presentation, students apply their knowledge to a case study.

Duration: 60-90 min

Requirements for the awarding of credit points

The performances are graded and must be completed with a minimum grade of sufficient (4.0).

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Literature

Balzert, H. (2008): Lehrbuch der Softwaretechnik: Softwaremanagement, 2. Auflage, Heidelberg: Spektrum Akademischer Verlag.

Balzert, H. (2009): Basiskonzepte und Requirements Engineering, 3. Auflage, Heidelberg: Spektrum Akademischer Verlag.

Richard Kastner, Dean Leffingwell: Safe 5.0 Distilled: Achieving Business Agility With the Scaled Agile Framework. Addison Wesley, 2020.

Ludewig, J., Lichter, H. (2013): Software Engineering Grundlagen, Menschen, Prozesse, Techniken, 3. korrigierte Auflage, Heidelberg: dpunkt-Verlag.

Pichler, R. (2009): Scrum - Agiles Projektmanagement erfolgreich einsetzen, Heidelberg: dpunkt-Verlag.

Pohl, K.; Rupp, C. (2015): Basiswissen Requirements Engineering, 4. überarbeitete Auflage, Heidelberg: dpunkt-Verlag.

Röpstorff, S., Wiechmann, R. (2016): Scrum in der Praxis, 2. aktualisierte Auflage. Heidelberg: dpunkt.verlag.

Sommerville, I. (2018): Software Engineering, 10. aktualisierte Auflage, München: Pearson.

Spitzcok, N.; Vollmer, G., Weber-Schäfer, U. (2014): Pragmatisches IT-Projektmanagement, 2. aktualisierte und überarbeitete Auflage, Heidelberg: dpunkt-Verlag.

Vollmer, G. (2017): Mobile App Engineering, Heidelberg: dpunkt-Verlag.

Winkelhofer, G. (2005): Management- und Projekt-Methoden, 3. Auflage, Berlin, Heidelberg: Springer.

Softwaretechnik D (Qualitätssicherung und Wartung)
WP

4 SWS

5 ECTS

Number
46264
Language(s)
de
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

After successfully completing the module, students will be able to:

Knowledge and understanding:

describe the structure of quality models
name different heuristics for test case generation
describe the limitations of program tests
describe different integration strategies
Name typical sources of error
classify quality assurance measures

Use, application and generation of knowledge:

distinguish between analytical and constructive measures
select metrics to measure quality
organize quality assurance measures
execute manual test procedures
execute analytical test procedures
organize software maintenance

Communication and cooperation:

Specify non-functional requirements
Organize quality assurance processes

Scientific self-image / professionalism:

to achieve a defined level of quality
select and use suitable tools (constructive quality assurance)

Quality models
Sources of error
Constructive measures
Manual inspection methods
Tools
Black box test
White box test
Metrics
Static code analysis
Test management
Automation (software infrastructure)
Maintenance and care

Teaching methods

Lecture in interaction with the students, with blackboard writing and projection

Solving exercises in individual or team work

Solving practical tasks in the practical course in individual or team work

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

Written written examination (between 60 and 90 minutes)

Requirements for the awarding of credit points

Passed written exam

Applicability of the module (in other degree programs)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor's degree in Business Informatics

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Computer Science Dual

Bachelor of Medical Informatics Dual

Bachelor of Computer Science

Literature

Balzert, H.; "Lehrbuch der Softwaretechnik, Softwaremanagement", Spektrum Akademischer Verlag, Heidelberg, 2008

Binder, R.V.; "Testing Object-Oriented Systems", Addison-Wesley, Boston, 2000

Hoffmann, D.W.; "Software-Qualität", Springer Vieweg, Berlin, 2013

Liggesmeyer, P.; "Software-Qualität", Spektrum Akademischer Verlag, Heidelberg, 2009

Ludewig, J.; Lichter, H.; "Software Engineering", dpunkt.verlag, Heidelberg, 2023

Spillner, A.; Linz, T.; "Basiswissen Softwaretest", dpunkt.verlag, Heidelberg, 2024

Sneed, H.M.; Seidl, R.; Baumgartner, M.; "Software in Zahlen", Hanser, München, 2010

Virtualisierung und Cloud Computing
WP

4 SWS

5 ECTS

Number
46810
Language(s)
en
Duration (semester)
1
Contact time
60 h
Self-study
90 h

Learning outcomes/competences

Technical and methodological skills:

Model object-oriented extensions using EER models and implement them in relational databases.
Implement complex user views and stored procedures for exemplary application scenarios.
Explain the 5-level model of a database management system.
Explain concepts of storage and access management.
Use examples to apply the methods of access optimization and transaction management.

Social skills:

Developing, creating, communicating and presenting database applications in small groups

Database implementation

Storage management
Logical and physical access optimization
Transaction management
Distributed databases
Performance optimization and SQL tuning

Development of database applications

Data modeling (EER model and logical design of object-oriented concepts)
Limitations of the relational model
Object-relational mapping frameworks
Ensuring data integrity and data protection (view hierarchies, stored procedures, triggers)
Conception, design and implementation of database applications in JAVA

Teaching methods

Solving practical exercises in individual or team work

Internship accompanying the lecture

Processing programming tasks on the computer in individual or team work

Active, self-directed learning through Internet-supported tasks, sample solutions and accompanying materials

Exercises or projects based on practical examples

The lecture is offered as a video

Inverted teaching (inverted classroom)

Participation requirements

See the respective valid examination regulations (BPO/MPO) of the study program.

Forms of examination

The module examination consists of a written exam (60-90 minutes) and coursework during the semester.
In the written exam, students should demonstrate basic knowledge of theoretical concepts, database architecture, performance optimization and development of database applications and demonstrate their skills in solving small application problems.

Through semester-long examinations (project-related work), students should design, develop, implement and present a database application for a self-chosen application scenario.

Requirements for the awarding of credit points

The performances are graded and must be passed with a total grade of 4.0. The performances consist of:

passed written examination (80%)
successful mini-project (project-related work) (20%)

Applicability of the module (in other degree programs)

Bachelor of Business Informatics

Bachelor of Software and Systems Engineering (dual)

Bachelor's degree in Software and Systems Engineering (dual)

Bachelor of Computer Science

Bachelor's degree in Medical Informatics

Bachelor of Medical Informatics Dual

Bachelor of Computer Science Dual

Literature

R. Elmasri, S. Navathe, Grundlagen von Datenbanksystemen, 2009

A. Kemper, A. Eickler, Datenbanksysteme (Eine Einführung), 2015

G. Saake, K.-U. Sattler, A. Heuer, Datenbanken Implementierungstechniken, 2011

R. Niemiec, Oracle database 12c release 2 performance tuning tips & techniques, 2017

R. Panther, SQL-Anfragen optimieren, 2014

6. Semester of study

Thesis mit Kolloquium
PF

4 SWS

15 ECTS

Number
103
Duration (semester)
1
Contact time
60 h
Self-study
120 h

Learning outcomes/competences

Upon completion of the module, students will be equipped with a robust theoretical framework, practical negotiation skills, and well-developed social and autonomous competencies, preparing them to excel as strategic negotiators in their professional careers.
The module will comprise a balanced mix of lectures, case studies, interactive simulations, and guest lectures from industry experts. Theoretical foundations will be covered in lectures, followed by practical applications through case studies and simulations. Group discussions and reflections will be encouraged to enhance social competencies and autonomy.

2.1 Professional Competencies
2.1.1 Knowledge
The Students can/know/apply

that negotiations are omnipresent and happen every day many times
recent negotiation principles and techniques for various settings
that negotiations are games that can be modelled with the help of game theory
how to successfully negotiate in various real-world environments especially in the procurement context
optimally negotiate when the rules of the game are given
optimally influence the rules of the game in a favourable way
optimally design the rules of the game
that negotiations base on human interaction which is prone to human errors
the extent to which artificially intelligence can support take over negotiations

Part a.) Theoretical framework:
Students understand the foundational theories of negotiations, including game theory, behavioral economics, and mechanism design.
Attendants analyze and apply concepts such as Nash equilibrium, Bayesian games, signaling, and repeated games in negotiation scenarios under various levels of information.
Students grasp the nuances of human behavior in negotiations, considering rational and behavioral perspectives.

Part b.): Case based applications:

Students apply theoretical frameworks to real-world negotiation cases, negotiation simulations and experiments.
They synthesize knowledge from game theory, behavioral economics, and mechanism design to analyze and solve complex negotiation challenges.
Attendants evaluate the role of artificial intelligence in negotiation processes and its practical applications.

2.1.2 Skills
The Students can/know/apply

Demonstrate proficiency in strategic negotiation planning and preparation
Apply various negotiation styles, adapting to different contexts (cooperation, competition, etc.)
Utilize game theory concepts to model rational behavior and make strategic decisions in negotiations
Design negotiations to achieve predefined targets using mechanisms and negotiation design
Leverage insights from behavioral economics to navigate and manage negotiation dynamics effectively

2.2 Personal Competencies
2.2.1 Social Competencies
The Students can/know/apply

Effectively communicate and build rapport in negotiation scenarios
Collaborate with diverse stakeholders in negotiation processes
Demonstrate empathy and active listening skills to understand the perspectives and interests of others
Manage emotions, both their own and others', during negotiations

2.1.2 Autonomy
The Students can/know/apply

Independently analyze and assess negotiation situations, considering both theoretical frameworks and practical insights
Make informed decisions in negotiation scenarios, considering the broader implications
Demonstrate autonomy in negotiation planning and execution, adapting strategies to dynamic environments
Reflect on personal negotiation styles and continuously improve based on feedback and self-assessment

Part a.): Theoretical Framework

A Primer on Negotiations
- Why Negotiations matter
- What Negotiations are about
  - Cooperation - Jointly Increase Size of the Pie
  - Competition - Split the Pie Among the Players
  - Bargaining Power
  - Rules
  - The Role of Information
  - Commitment
  - Credibility
  - Rationality
  - Bounded Rationality
  - Emotions
- Which Types of Negotiations exist
  - Bilateral Negotiations - 1:1
  - Auctions - 1:N
  - Hybrid Forms of Negotiations
Modeling Rational Behavior in Negotiations: A Game Theoretical View
- Static Games of Complete Information
  - Normal Form Games and Nash Equilibrium
  - Applications
    - Cournot Competition
    - Betrand Competition
  - Repeated Games
  - Dynamic Games of Complete but Imperfect Information
- Static Games of Incomplete Information
  - Static Bayesian Games and Bayesian Nash Equilibrium
  - Applications
    - Cournot Duopoly with Incomplete Information
    - Auctions
  - The Revelation Principle
- Dynamic Games of Incomplete Information
  - Perfect Bayesian Equilibrium
  - Signaling Games
  - Applications
    - Cheap-Talk Games
    - Sequential Bargaining under Asymmetric Information
    - Reputation in the Finitely Repeated Prisonners' Dilemma
Modeling Human Behavior in Negotiations: A Behavioral Economics View
- Where Human Behavior Departs from Homo Oeconomicus
- Which Types of Errors Exist
- How to Minimize Errors in Negotiations
- How to Make Use of Errors in Negotiations
Designing negotiations to optimally achieve some predefined target
- Introduction to Mechanism and Negotiation Design - Art and Science
- Prerequisites
- Approaches
- Real cases
- The Role of Artificial intelligence

Part b.): Case based applications

Negotiations simulations and experiments in the sales and procurement context

Further recent real case topics related to negotiations and applied game theory

Teaching methods

Lectures,

Seminar,

Case studies

Experiments

Results-oriented negotiations

Participation requirements

Formal: none
Knowledge and Competencies: none

Forms of examination

Final case work / presentation (50 %)

Participation in experiments (40%)

Oral participation (10 %)

Requirements for the awarding of credit points

Minimum achievement of 50 percentage points - calculated as the sum of the achievements in the assessment areas above.

Applicability of the module (in other degree programs)

M.A. Business Management, M.A. International Management

Importance of the grade for the final grade

2 semester course of studies: 10 % (6/30) x 50
3 and 4 semester course of studies: 6,7 % (6/60) x 67

Literature

Berz, G. (2022). Game Theory Bargaining and Auction Strategies, Palgrave Macmillan UK.

Brams, S. J., Mitts, J. (2013), Law and Mechanism Design: Procedures to Induce Honest Bargaining,68 NYU Annual Survey of American Law 729.

Brandenburger, A. M., Nalebuff, B. J (1998), Co-opetition, Crown Business.

Cartwright, E. (2017), Behavioral Economics: An Introduction, Routledge.

Creutzmann, J. B. (2021), Strategic Relevance and Application of the Mechanism Design Theory at the example of selected European Private Procurement Auctions in a B2B-context, Bachelor Thesis University of Twente.

Dixit, A. K., Nalebuff, B. J. (1991), Thinking Strategically: The Competitive Edge in Business, Politics, and Everyday Life, W. W. Norton & Company.

Fisher, R., Ury, W. and Patton, B., (1992), Getting To Yes: Negotiating Agreement Without Giving In, Houghton Mifflin Company.

Fudenberg, D., Tirole, J. (1991), Game Theory, The MIT Press.

Gibbons, R. (1992), Game Theory for Applied Economists, Princeton University Press.

Kahneman, D. (2013) Thinking, Fast and Slow, Penguin Books.

Levine, D. K. (2018), Behavioral Economics: Experiments and Models, Princeton University Press.

Osborne, M. J., Rubinstein, A. (1994), A Course in Game Theory, MIT Press.

Schulze-Horn, I., Pulles, N., Schiele, H., Scheffler, P. (2018), Using mechanism design theory in negotiations to improve purchasing performance, International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 11(6), pages 777-800.

Schulze-Horn, I., Hueren, S., Scheffler, P., Schiele, H., (2020), Artificial Intelligence in Purchasing: Facilitating Mechanism Design-based Negotiations, Applied Artificial Intelligence, Vol. 34, pages 618-642.

Watson, J. (2010), Notes on Mechanism Design and Contract, Lecture Notes ETH Zurich.

Wolfstetter, E. (1999), Topics in Microeconomics - Industrial Organization, Auctions, And Incentives, Cambridge University Press

Study plan

Algorithmen und Programmierung

Mathematik für Informatik 2

Datenvisualisierung

Data Science Datenbanken

ERP 2

Thesis mit Kolloquium

Algorithmen und Programmierung – Projektwoche

Technisches Englisch

IT-Recht

Grundlagen des Maschinellen Lernens

Data Science Projekt

BWL

Datenbanken 1

Mathematik für Informatik 4 Data Science

Informationssicherheit

Informatik und Gesellschaft

Mathematik für Informatik 1

Lern- u. Arbeitstechniken

Mensch Computer Interaktion

Kommunikations- und Rechnernetze

Projektarbeit

Rechnerstrukturen und Betriebssysteme 1

Mathematik für Informatik 3

Programmierkurs Anwendungsentwicklung

Künstliche Intelligenz

Seminar (Inhalt)

Theoretische Informatik

Objektorientierte Programmierung und Datenstrukturen

Programmierkurs Python und R

Softwaretechnik 2

Compulsory elective modules (26)

Compulsory elective modules (2)

Objektorientierte Programmierung und Datenstrukturen – Projektwoche

Seminar (Methodik)

Studium Generale

Rechnerstrukturen und Betriebssysteme 2

Softwaretechnik 1

Web-Technologien

Studium Generale

Compulsory elective modules 1. Semester

Ausgewählte Aspekte der Data Science 1

Ausgewählte Aspekte der Data Science 2

Compulsory elective modules 2. Semester

Compulsory elective modules 3. Semester

Compulsory elective modules 4. Semester

Compulsory elective modules 5. Semester

Componentware

Adaptive Systeme

Anerkannte Wahlpflichtprüfungsleistung

Anerkannte Wahlpflichtprüfungsleistung

Anerkannte Wahlpflichtprüfungsleistung

Angewandte Logiken

Ausgewählte Aspekte der Data Science 3

Ausgewählte Aspekte der Informatik 1 (Katalog Informatik gültig für Data Science)

Ausgewählte Aspekte der Informatik 2 (Katalog Informatik gültig für Data Science)

Ausgewählte Aspekte der Informatik 3 (Katalog Informatik gültig für Data Science)

Datenethik und Datenschutz

Digitale Bildverarbeitung

ERP 1 (Standardsoftware)

Effiziente Algorithmen und Datenstrukturen

Fortgeschrittene Informationssicherheit

IT-Servicemanagement

Informations- und Business Performance Management

Informationssysteme im Gesundheitswesen

Kooperative Systeme

Mobile App Engineering

Moderne Datenbanken

Numerische Algorithmen

Robotik

Softwaretechnik C (Softwaremanagement)

Softwaretechnik D (Qualitätssicherung und Wartung)

Virtualisierung und Cloud Computing

Compulsory elective modules 6. Semester

Module overview

1. Semester of study

Algorithmen und ProgrammierungPF 5 SWS 5 ECTS

Learning outcomes/competences

Contents

Teaching methods

Algorithmen und Programmierung
PF

5 SWS

5 ECTS

Algorithmen und Programmierung – Projektwoche
PF

2 SWS

2.5 ECTS

BWL
PF

4 SWS

5 ECTS

Mathematik für Informatik 1
PF

4 SWS

5 ECTS

Rechnerstrukturen und Betriebssysteme 1
PF

4 SWS

5 ECTS

Theoretische Informatik
PF

4 SWS

5 ECTS

Ausgewählte Aspekte der Data Science 1
WP

4 SWS

5 ECTS