Department of Applied Computer Sciences

Data Science and Artificial Intelligence

My Studies

 

Current Curriculum

1. Semester

Applied Computer Science 1 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (IL) | Coursecode: 200807101 Introduction to Data Science 3 SWS 5 ECTS
Area 1: Introduction to Data Science
Introduction and Basics 2 | Practical (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 …
Statistics 1 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 | Lecture/Practical (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 (PA) | 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 | Lecture/Practical (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 | Lecture/Practical (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 | Modul/Final Examination (FA) | Coursecode: 0 Master's Examination 0 SWS 3 ECTS
Master's Thesis and Master's Examination | Master's Thesis (MA) | Coursecode: 200807404 Master's Thesis 0.5 SWS 20 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 "