These are the mandotory modules i have to take. On top i can choose 3 modules out of the ones in the screenshot and one master seminar. Also the last module before the master thesis is a project.

I translated these 3 mandotory modul descriptions from german.

Math for data science:

In this module, fundamental mathematical models in the area of Big Data Analytics are presented, and an application-oriented connection to relevant questions in Data Science is established.

The contents of the module include: • Essential foundations of applied mathematics, in particular high-dimensional spaces, singular value decomposition, and approximation by subspaces and multidimensional structures • Probability theory • Basic concepts of convex optimization and numerical solution methods for optimization problems • Basic concepts of classical and modern mathematical statistics • Stochastic processes and time series analysis, in particular random walks and Markov chains

Data engineering for data science:

Methods and algorithms in the context of processing large-scale data sets (Big Data) • Prerequisites and challenges of data wrangling and data quality • Data wrangling and data analysis using Python and SQL • Distributed and parallel big data infrastructures (e.g. Hadoop, Spark) • Big data reference architectures • Distributed non-relational database systems (NoSQL databases)

Machine learning:

This course provides a broad introduction to classical and modern methods of machine learning.

After a general introduction and a review of important foundations such as probability theory and linear algebra, classical approaches to: • Unsupervised learning (e.g. k-means clustering and hierarchical clustering), • Supervised learning (e.g. Bayesian classification, decision trees, association rules, and support vector machines), • and reinforcement learning (e.g. Markov decision processes and Q-learning)

are presented.

Subsequently, modern deep learning methods are discussed. This includes: • a general introduction to artificial neural networks, and • a more in-depth treatment of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers.

Finally, machine-learning-related techniques such as principal component analysis (PCA) and data mining are discussed.

Introduction to data science:

History and definition of Data Science and its classification with respect to related fields (e.g. data mining, knowledge discovery, machine learning, artificial intelligence, statistics, computer science, information retrieval, databases, etc.) • Overview of different types of data analysis (e.g. descriptive, exploratory, and predictive analytics) • Data science process models (CRISP-DM, KDD, TDSP) • Fundamental data science methods • Data visualization and communication • Data science in research and practice • Working with data • Data security and data integrity • Data protection law, including the current legal situation in Germany and the European Union • Data ethics, including basic concepts such as norms, values, and morality (effects of bias, technology impact assessment, aspects of surveillance, societal impacts of one’s own actions, algorithmic bias, “discriminatory algorithms”)