Structure and Content

The program is structured into the five blocks Fundamentals, Data Management, Data Analytics, Projects and Seminars, and the Master’s Thesis.

Disclaimer: Although we try to keep this page up-to-date with the module catalog and the study regulation, the information displayed on this page is not legally binding.

Example study plan (note: this is just an example - your actual study plan may vary depending on the semester in which you start, your preferences, etc.)


Fundamentals (0 – 14 ECTS)

The goal of the fundamentals block is to align the previous knowledge of students from different degree programs. Graduates from computer science and mathematics acquire the required knowledge in empirical research (in particular, data collection and multivariate statistics). Graduates from the social sciences and other fields acquire the required knowledge in computer science (in particular, programming and database technology).

Data Management (24 – 36 ECTS)

One of the central challenges in the Big Data area is to handle the enormous amount, speed, heterogeneity, and quality of the data collected in industry, the public sector, and science. The Data Management block covers methods and concepts for obtaining, storing, integrating, managing, querying, and processing large amounts of data. The block includes courses on modern data management technology (such as parallel database systems, Spark, and NoSQL databases), data integration, information retrieval and search, software engineering, and algorithms.

Data Analytics (30 – 54 ECTS)

The Data Analytics block forms the core of the study program. It provides courses ranging from data mining, machine learning, and decision support, over text analytics and natural language processing, to advanced social science methods such as cross-sectional and longitudinal data analysis. The range of methodological courses is enhanced by courses on optimization, visualization, mathematics and information, and algebraic statistics.

Projects and Seminars (12 – 16 ECTS)

The Projects and Seminars block introduces students to independent research and teaches the skills necessary to successfully participate in and contribute to larger data science projects. The block consists of research seminars, individual projects, team projects, as well as data science competitions. The projects are conducted jointly with industrial partners and/or support ongoing research efforts of participating institutes.

Master’s Thesis (30 ECTS)

In the master thesis, students apply what they learned throughout the program. The master thesis has a duration of 6 months. Students are encouraged to write their thesis either in the context of research projects conducted by participating institutes or together with an industrial partner.

Courses as of April 2017

Please find the preliminary course catalog of the 2017 spring term here!

The following tables give an overview of the courses in the blocks Fundamentals, Data Management, and Data Analytics as of January 2017. Students are allowed attend the combination of courses of their choice in order to achieve the required ECTS points per block.

Please note that since this list is under current revision, further courses will be added, while others also be removed in the future. The first official module handbook (Modulkatalog) for the new program will be published in Fall 2016.

Fundamentals (0 – 14 ECTS)

Data Acquisition*Dr. Sean Carey6X
Database Technology*Prof. Dr. Paulheim6X
Multivariate Analysis*Prof. Gschwend6+2X
Programming Course*Dr. Rost6X

Data Management (24 – 36 ECTS)

Advanced Software EngineeringProf. Dr. Atkinson6X
Algorithmik*Prof. Dr. Krause6X
AnfrageoptimierungProf. Dr. Moerkotte6X
Data SecurityProf. Dr. Armknecht6X

Datenbanksysteme II

Prof. Dr. Moerkotte6X
Information Retrieval and Web SearchProf. Dr. Ponzetto6X
Large Scale Data ManagementProf. Dr. Gemulla6X
Model-driven DevelopmentProf. Dr. Atkinson6X
Semantic Web TechnologiesProf. Dr. Paulheim6X
Web Data IntegrationProf. Dr. Bizer6X

Data Analytics (30 – 54 ECTS)

Advanced Quantitative Methods Prof. Gschwend6+2X
Algebraische StatistikProf. Dr. Seiler8see here
Applied TopologyProf. Dr. Roggenkamp8see here
Computational FinanceProf. Dr. Neuenkirch5from 2018
Cross Sectional Data AnalysisProf. Dr. Gautschi6+3X
Data Mining I*Prof. Dr. Bizer / Prof. Dr. Paulheim6XX
Data Mining IIProf. Dr. Paulheim6X
Data Mining and MatricesProf. Dr. Gemulla6X
Decision SupportProf. Dr. Stuckenschmidt6X
Higher Level Computer VisionProf. Dr.-Ing. Keuper6X
Hot Topics in Machine LearningProf. Dr. Gemulla6X
Image ProcessingProf. Dr.-Ing. Keuper6X
Longitudinal Data AnalysisProf. Dr. Wolbring6X
Mathematics and InformationProf. Dr. Seiler8see here
Mathematische VisualisierungProf. Dr. Seiler8see here
Non-linear OptimizationProf. Dr. Göttlich6X
OptimizationProf. Dr. Göttlich8X
Research DesignProf. Dr. Kreuter6+3X

Text Analytics

Prof. Dr. Ponzetto6X
Web MiningProf. Dr. Bizer6X

Projects and Seminars

Individual Data Science Projectvarious8XX
Teamproject Data Sciencevarious12XX

  Courses marked with an asterisk (*) are particularly suitable for first semester students.