Data Science and Artificial Intelligence

Applied Computer Science 1 | Lecture/Practical (IL) | Coursecode: 200807108

Scripting for Data Scientists

5.00

ECTS

3.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

5.00

ECTS

3.00

SWS

Area 1: Introduction to Data Science

Introduction and Basics 2 | Practical (UE) | Coursecode: 200807109

Repetitorium – review course

5.00

ECTS

3.00

SWS

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 …

Statistics 1 | Lecture/Practical (IL) | Coursecode: 200807104

Descriptive Statistics

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

Applied Computer Science 2 | Lecture/Practical (IL) | Coursecode: 200807208

Agent-based programming

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

5.00

ECTS

3.00

SWS

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

5.00

ECTS

3.00

SWS

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

5.00

ECTS

3.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

Applied Computer Science 3 | Lecture/Practical (IL) | Coursecode: 200807305

Cloud computing for data scientists

5.00

ECTS

3.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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 informatio – 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 ..

Computational Intelligence 3 | Lecture/Practical (IL) | Coursecode: 200807304

Swarm intelligence and evolutionary algorithms

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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

7.50

ECTS

1.00

SWS

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

Cross-professional Qualifications 2 | Lecture/Practical (IL) | Coursecode: 200807401

Ethics, Compliance and Data Protection

2.50

ECTS

2.00

SWS

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

2.50

ECTS

2.00

SWS

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:

Master's Examination

3.00

ECTS

0.00

SWS

Master's Thesis and Master's Examination | Master's Thesis (MA) | Coursecode: 200807404

Master's Thesis

20.00

ECTS

0.50

SWS

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

2.00

ECTS

1.50

SWS

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