Department of Applied Computer Sciences

Data Science and Artificial Intelligence

My Studies



1. Semester

Applied Computer Science 1 | Integrated course (iL) | Coursecode: 200807108 Scripting for Data Scientists 3 SWS 5 ECTS
Area 1: Programming / scripting
- Programming paradigms
- Data types
- Elementary commands
- Operators and control structures
- Functions and libraries
- Regular expressions
- Clean coding and debugging
Area 2: Data-based applications
- Import and export of data
- Elementary data handling
Area 3: Tools
- Version control systems
- development environments
Applied Mathematics 1 | Integrated course (iL) | Coursecode: 200807103 Graph theory and system dynamics 2 SWS 2.5 ECTS
Area 1: Graph theory
- Basic terms of graphs
- Incidence matrix, degree matrix, adjacency matrix, distance matrix, Laplace matrix
- Relationship of graphs
- Planar and bipartite graphs
- Euler and Hamiltonian graphs
- Basics of directed graphs
Area 2: System dynamics
- Overview of modeling and simulation
- Systems science basics
- Effect graphs, effect matrices and pulse models
- Eigenvalue problem, matrix norms, singular values and diagonalization
- Markov chains
- Cybernetic and control engineering basics
- Linear and non-linear differential equations
- Taylor series and linearization
- Initial value problems and numerical integration
- Equilibria and stabilities of differential equations
- Basics of event-oriented simulation
Applied Mathematics 1 | Integrated course (iL) | Coursecode: 200807102 Information and coding theory 2 SWS 2.5 ECTS
Area 1: Information Theory & Signal Processing
- Weaver model of communication
- Statistical properties of natural languages
- Shannon entropy
- Basic concepts of signal processing
- Fourier series and integral transformations
Area 2: Number theory and coding theory
- Number systems, divisibility, prime numbers, Chinese remainder theorem
- Coding (Huffman code, Hamming distance, Grey code, ...)
- Check digits and hash codes
- Error correcting codes
- Data compression
Area 3: Cryptography
- History and basic concepts of cryptography
- Symmetrical vs. asymmetric methods
- Important procedures (RSA, AES, ...)
- Cryptographic hashing
Database Systems 1 | Integrated course (iL) | Coursecode: 200807106 Database basics and query language 2 SWS 2.5 ECTS
Area 1: Introduction and basic terms
- Database models including historical development
- Architectural layers
Area 2: Relational databases
- Basic terms of the relational data model
- Data modeling using the entity relationship model
- Integrity conditions and normal forms
- Denormalization
Area 3: SQL
- Relational operators
- Data Query Language (DQL)
- Data Manipulation language (DML)
- Data Definition Language (DDL)
- Data Control Language (DCL)
Area 4: Special topics
- Distributed and federated database systems
- NoSQL databases
- Data security
Database Systems 1 | Integrated course (iL) | Coursecode: 200807107 Relational Database Management 2 SWS 2.5 ECTS
Area 1: Basic topics
- Installation and setup of a relational database system
- Creation of relational databases and import/export of data records
- Rights concept and user administration
- SQL statements (DQL, SML, DDL, DCL)
- Views and indexes
Area 2: Advanced topics
- Stored procedures, functions, transactions and triggers
- File groups, FileTables, partitions and cursors
- Memory optimization and encryption
- Spatial and hierarchical data types
Introduction and Basics 1 | Integrated course (iL) | Coursecode: 200807101 Introduction to Data Science 3 SWS 5 ECTS
Area 1: Introduction to Data Science
Introduction and Basics 2 | Tutorial (Ue) | Coursecode: 200807109 Repetitorium - review course 3 SWS 5 ECTS
Repetition of important basics for your studies, such as:
I. Repetition of Mathematics and Higher Mathematics
Area 1: Basic mathematical terms
- Set theory and sets of numbers
- Solve equations and inequalities
- Elementary functions
- Compute with complex numbers
- Metric spaces
Area 2: Elementary Analysis
- Sequences and rows, limit value concept
- Differential calculus, extreme value problems, L'Hospital's Rule
- Integral, simple integrals, gamma function
Area 3: Basic concepts of linear algebra
- Vectors and matrices
- Solution of linear systems of equations
- Vector spaces including functional spaces
II. Information Science Repetitorium
Area 1: basic terms
- Data, knowledge and information management
- Information retrieval
Area 2: cognition
- Neurons, synapses, neurotransmitters, neuronal circuits
- Neuronal storage of information, macroscopic structure of the brain
- Cognitive psychological basics
Area 3: Communication
- Model / theorie of signal and message transmission
- Communication models and theories
Statistics 1 | Integrated course (iL) | Coursecode: 200807104 Descriptive Statistics 2 SWS 2.5 ECTS
Area 1: Introduction and parameters
- Overview of the sub-disciplines of statistics
- Level of measurements (scale of measure)
- Location, dispersion and association measures
- Basics of statistical visualization (especially boxplots and scatterplots)
Area 2: regression
- linear regression
- Linear transformable nonlinear regression
- Logistic regression
Area 3: Time series analysis
- Trends and seasonal components
- Autocorrelation
- Heteroscedasticity
Statistics 1 | Integrated course (iL) | Coursecode: 200807105 Probability theory and inductive statistics 2 SWS 2.5 ECTS
Area 1: Probability Theory
- Basic concepts of probability theory
- Limit theorems
- Conditional probabilities and Bayes theorem
- Basic concepts of combinatorics
- Important discrete and continuous univariate distributions
Area 2: Inductive statistics
- Samples and confidence intervals
- Data reduction and sampling theorem
- Hypothesis tests for parametric and nonparametric distributions
- Resampling (bootstrapping, cross-validation, ...) and Monte Carlo method
- Maximum likelihood method

2. Semester

Applied Computer Science 2 | Integrated course (iL) | Coursecode: 200807208 Agent-based programming 2 SWS 2.5 ECTS
Part 1: Fundamentals of agent-based programming
- Cellular automata, self-organization and emergences
- Properties of agents or agent-based models
- Description of agent-based models using the ODD protocol
- Overview of known agent-based models
Part 2: Programming and evaluation of agent-based models
- Introduction to the conception and programming of agent-based models
- Introduction to the evaluation of agent-based models / simulations
- Advanced topics in agent-based modeling
Applied Computer Science 2 | Integrated course (iL) | Coursecode: 200807209 High Performance Computing 2 SWS 2.5 ECTS
Part 1: Basics
- Overview and definition of terms
- Processor architectures (CPU, GPU, TPU, ...) and relevant interfaces
Part 2: Hardware virtualization
- Platform virtualization
- Relevant cluster frameworks in the context of hardware virtualization
- Storage virtualization
Part 3: Operating system virtualization
- Container virtualization
- Relevant cluster frameworks in the context of operating system virtualization
Applied Mathematics 2 | Integrated course (iL) | Coursecode: 200807203 Data Structures and Algorithms 2 SWS 2.5 ECTS
Part 1: Classic data structures and algorithms
- Computability, Turing machine and Optimal Stopping
- Runtime considerations and Landau notation
- Basic tasks of algorithm development
- Simple and advanced data structures
- Simple algorithms (backtracking, bubblesort, ...)
- Divide and conquer principle (including dynamic programming)
Area 2: Advanced algorithms
- Special features when accessing sequentially stored data
- Priority queues and self-organizing data structures
- Basics of lossy compression of data
- Basics of Fast Fourier Transform
- Single pass algorithms
- Kalman filter
Applied Mathematics 2 | Integrated course (iL) | Coursecode: 200807202 Optimization and Numerics 2 SWS 2.5 ECTS
Part 1: Aspects of numerics
- Numerical presentation on the computer
- Type and reduction of numerical errors
- Conditioning problems
- Numerical differentiation and numerical quadrature
- Numerical solving of systems of equations (including Newton's method)
- Pivoting and matrix decomposition (LU, QR, ...)
Part 2: Optimization
- Basic aspects of optimization tasks
- One- and multi-dimensional extreme value tasks
- 1st order descent procedure (steepest descent, impulse methods, ...)
- 2nd order descent procedure (Newton and Newton-style procedures, ...)
- Conjugate gradients
- Linear optimization, simplex algorithm, MILP problems
- Optimization with constraints (long-range approach including KKT conditions)
- Multi-criteria optimization (including Pareto analysis)
- Special methods of stochastic optimization (e.g. simulated anealing)
Computional Intelligence 1 | Integrated course (iL) | Coursecode: 200807201 Neural Networks I: Architectures 3 SWS 5 ECTS
Area 1: Basics and tools
- Repetition of natural neural networks
- Perceptron and linear separability
- Basic structures of artificial neural networks
- Multilayered Perceptron and error back propagation
- Hopfield Networks
- Markow Chain Monte Carlo Methods
- Tensors and tensor calculation
- Common frameworks for artificial neural networks
Area 2: Fields of application
- Time Series prediction
- Handwriting Recognition
- Associative Pattern Recognition
Area 3: Advanced Architectures
- Boltzmann Machines
- Self-organizing Cards
- Autoencoder
- Basics of Convolutional Neural Networks
- Basics of Recurrent Neural Networks
Database Systems 2 | Integrated course (iL) | Coursecode: 200807207 Analytical Information Systems 3 SWS 5 ECTS
Part 1: ETL or ETL processes
- Basics of ETL resp. ETL processes
- Planning and creation of ETL workflows
Part 2: Multidimensional resp. OLAP databases
- Basics of multidimensional resp. OLAP databases
- Planning and creation of multidimensional resp. OLAP databases
- Access to multidimensional resp. OLAP databases
- Introduction to the query language MDX
- Data mining using multidimensional resp. OLAP databases
Part 3: Business Intelligence resp. Business Analytics
- Introduction to business intelligence and business analytics
- Overview of important solutions in the area of business intelligence and business analytics
- Overview of important solutions in the area of self-service BI
Statistics 2 | Integrated course (iL) | Coursecode: 200807204 Multivariate statistics and data mining 3 SWS 5 ECTS
Part 1: Structure-discovering processes:
- Principal Component Analysis
- Exploratory factor analysis
- Nearest neighbor classification
- Cluster analysis
- Partial Least Squares regression
- Support vector machines
- Multidimensional scaling
Part 2: Structural inspections:
- Multivariate linear, nonlinear and logistic regression
- LASSO (least absolute shrinkage and selection operator)
- Multivariate time series analysis (including structural break analysis)
- Structural equation models
- Discriminant analysis
- Analysis of variance
- Confirmatory factor analysis
Part 3: Text mining
- Word frequencies and correlations
- Grouping / clustering of texts
Statistics 3 | Integrated course (iL) | Coursecode: 200807206 Advanced information visualization 2 SWS 2.5 ECTS
Part 1: Basics of visualization
- Basics of human processing of visual information
- Pitfalls and distortions in visualizations
- Standardization in the field of visualization
- Report and chart types and their properties
- Classic diagram types (area, bar, column, line, network diagrams, boxplots, scatterplots etc.)
- Modern diagram types (heat maps, tree maps, stream graphs, chord and sunburst diagrams etc.)
- Special types of diagrams (speedometer, waterfall diagrams, maps etc.)
- Text-based visualizations (word clouds, infographics, etc.)
Part 2: Advanced topics
- Animated visualizations
- Interactive visualizations
- Automated dynamic reporting
Statistics 3 | Integrated course (iL) | Coursecode: 200807205 Data quality and data cleansing 2 SWS 2.5 ECTS
Part 1: Preparation of data
- Reading in and working with data from different sources (CSV, XML, HTML, JSON, ...)
- Character sets or character set transformation
- Data type conversion and renormalization
- Duplicate detection and deduplication
- Complex transformations of data (especially pivoting and unpivoting)
- Complex filtering and sorting of data
Part 2: Erroneous and incomplete data
- Data quality analysis
- Smoothing discrete data
- Anomaly detection
- Singular and multiple imputation
Part 3: Continuous data
- Special features of audio, image and video data (or signal data)
- Transformations and discretization of continuous data
- Convolution and application of filters
- Smooth continuous data
- Compression of continuous data

3. Semester

Applied Computer Science 3 | Integrated course (iL) | Coursecode: 200807305 Cloud computing for data scientists 3 SWS 5 ECTS
Part 1: Fundamentals of cloud computing
- Overview and definition of terms
- IT architectures and IT service management
- Service deployment models (XaaS, Edge Computing, Fog Computing, ...)
- Security management and identity management
- Overview of important cloud computing providers
Part 2: Introduction to cloud computing
- Idenity management binding and synchronization
- Setup and configuration of simple cloud services
- Monitoring and cost management
Part 3: Data storage and data processing in the cloud
- Setup, configuration and deployment of selected storage services
- Setup, configuration and deployment of clusters for the distributed storage and processing of big data
- High-performance and scalable queries
Computational Intelligence 2 | Integrated course (iL) | Coursecode: 200807302 Advanced topics in artificial intelligence 2 SWS 2.5 ECTS
Part 1: Advanced KI-powered applications
- Semantic text analysis and text synthesis, natural language processing
- Biometric analysis
- Generation of synthetic data sets
- Other advanced KI-powered applications
Part 2: Methods of Artificial Intelligence in Practice
- Field of application as well as advantages and disadvantages of different KI methods
- Hybrid approaches (fuzzy neural approaches etc.)
- Selection of suitable AI methods for specific problems
- Typical mistakes and problems as well as their avoidance or reduction
- New approaches in artificial intelligence and computational intelligence
Computational Intelligence 2 | Integrated course (iL) | Coursecode: 200807301 Neural Networks II: Deep Learning 2 SWS 2.5 ECTS
Part 1: Advanced topics regarding neural networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
Part 2: Advanced applications of neural networks
- Handwriting and speech recognition
- Edge detection in pictures and videos
- Object recognition in pictures and videos
Part 3: Deep learning in practice
- Deep learning frameworks for CPU, GPU and TPU computing
- Planning, conception, setup as well as training and optimization of neural networks
Computational Intelligence 3 | Integrated course (iL) | Coursecode: 200807303 Decision theory and game theory 2 SWS 2.5 ECTS
Part 1: Preferences and Mechanism Design Theory
- Binary relations and preference orders
- Theory of disclosed preferences and conjoint analyzes
- Preference aggregation method and Arrow's impossibility theorem
- Gibbard-Satterthwaite theorem
Part 2: Decision Theory
- Decision-theoretical basic concepts
- Risk awareness and risk tendency
- Solution concepts for risk decisions
- Solution concepts for decisions in the event of uncertainty
Part 3: Non-cooperative game theory
- Basic concepts of non-cooperative game theory
- Static games with complete information
- Dynamic games with complete information
- Static games with incomplete information
- Dynamic games with incomplete information
- Auctions and auction theory
Part 4: Cooperative game theory
- Basic concepts of cooperative game theory
- Important solution concepts for cooperative games
Computational Intelligence 3 | Integrated course (iL) | Coursecode: 200807304 Swarm intelligence and evolutionary algorithms 2 SWS 2.5 ECTS
Part 1: Swarm intelligence
- Basics of swarm intelligence
- Examples of swarm-intelligent systems
- Basics of particle swarm optimization
- Conception and programming of swarm-intelligent models using agent-based programming
- Evaluation of swarm-intelligent models / simulations
Part 2: Genetic and evolutionary algorithms
- Basic principles of genetic and evolutionary algorithms
- Applications of genetic and evolutionary algorithms
- Use of evolutionary algorithms to evaluate agent-based models
- Basic principles of evolutionary game theory
- Basic principles of artificial immune systems
Cross-professional Qualifications 1 | Integrated course (iL) | Coursecode: 200807306 Business Development und Innovation 2 SWS 2.5 ECTS
Part 1: Basic business terms
- Controlling and accounting
- Investment and finance
- Organization, HR management and leadership
- Performance management
- Marketing , customer relationship management and logistics
- Legal framework
- Risk and risk management
Part 2: Strategic Analysis
- External analysis of macroeconomics, industry, sectors etc.
- Internal analysis of resources, stakeholders, governance, corporate culture etc.
- SWOT analysis
Part 3: Strategies and strategy development
- Business strategy vs. Corporate strategy
- Mergers & acquisitions and strategic alliances
- Strategy development in practice
Part 4: Innovation
- Innovation, entrepreneurship and intrapreneurship
- Software solutions for performing Monte Carlo Simulations
- Monte Carlo simulation as well as creation of business models and financial plans (especially P&L, cash flow planning)
- Rapid prototyping
Cross-professional Qualifications 1 | Seminar (Se) | Coursecode: 200807307 Scientific Methods and Writing 2 SWS 2.5 ECTS
Part 1: Philosophy of Science
- History of the philosophy of science
- Important theories resp. lines of thought in scientific theory
- Overview of scientific research methods
Part 2: Research processes
- Deriving research questions and hypotheses
- Conducting intensive research
- Design of the research project or decision regarding methodology
- Analysis, publication and presentation of gain of knowledge
- Working techniques and time management
Part 3: Publication and publication standards
- Clear and consistent writing style as well as gender-appropriate wording
- Citation and handling of literature management programs
- Property rights and ethical principles
- Structuring, formatting and visualization of publications
- Publicationvariants
- Quality assurance resp. reviews and peer reviews
- Rankings and impact factors
Project | Integrated course (iL) | Coursecode: 200807308 Project Management and Evaluation of Software Solutions 2 SWS 2.5 ECTS
Part 1: Fundamentals of R&D project management
- Basic terms and phases
- norms and standards
- methods and tools
- Basics of agile project management
- Communication, presentation and moderation
- crisis management
Part 2: Funding projects
- An important basis for funding projects
- Important funding agencies and funding channels
Part 3: Software-based project management
- Software for planning, controlling and controlling projects
- Software-based project management in practice
Part 4: Evaluation of software solutions
- Important evaluation criteria for software in the field of data science
- Established state-of-the-art platforms and software solutions
Project | Project thesis (PT) | Coursecode: 200807309 Project work 1 SWS 7.5 ECTS
Part 1: Implementation of data science projects
- Dealing with given requirements
- Development of different solution strategies
- Planning, implementation, control and controlling the project resp. project progress
- Teamwork including any conflict resolution
Part 2: Project documentation and dissemination of project results
- Creation of project documentation based on norms, standards and specifications
- Presentation and discussion of project and results

4. Semester

Cross-professional Qualifications 2 | Integrated course (iL) | Coursecode: 200807401 Ethics, Compliance and Data Protection 2 SWS 2.5 ECTS
Part 1: Ethics
- Ethical funamentals and problems
- Ethical consideration of big data and artificial intelligence
- Corporate social responsibility
Part 2: Data protection
- Basic terms and overview
- Data protection law
- General data protection regulation
- Enforcement in data protection
Part 3: Compliance or IT compliance
- Governance and compliance
- IT governance and IT compliance
- IT risks and IT risk management
Cross-professional Qualifications 2 | Integrated course (iL) | Coursecode: 200807402 Success Strategies for Data Scientists 2 SWS 2.5 ECTS
Part 1: Data Science in Practice
- Analysis of problems and selection of suitable methods and algorithms
- Discussion of the advantages and disadvantages of different methods and algorithms
Part 2: Best Practices and the future of Data Sciene
- Best practices in data science projects
- Avoiding typical pitfalls in data science projects
- Discussion of the status quo and the future of data science
Master's Thesis and Master's Examination | Master Thesis (MA) | Coursecode: 200807404 Master's Thesis and Master's Examination 0.5 SWS 23 ECTS
Part 1: Master's thesis
- Deriving research questions and hypotheses
- Conducting intensive research
- Design of the research project or decision regarding methodology
- Implementation of the planned research project
- Writing the master's thesis according to certain norms, standards and specifications
- Regular coordination with the supervisor of the master's thesis
Part 2: Master’s examination
- Presentation and defense of the master thesis
- Taking partial exams on important content relevant to the curriculum
Master's Thesis and Master's Examination | Seminar (Se) | Coursecode: 200807403 Seminar on the Master Thesis 1.5 SWS 2 ECTS
Part 1: Exposé for the master's thesis
- Preparation of the synopsis for the master's thesis according to certain norms, standards and guidelines
Part 2: Dissemination of the first results of the master thesis
- Presentation and defense of the first results of the master thesis
- Discussion about the first results of other master thesis projects
- give and take feedback and reflect "