Master Data Science (M.Sc.)

The Master's programme in Data Science (taught completely in English) is intended to give interested students the opportunity to consolidate and deepen their knowledge in the field of statistics and information technology at a demanding level. The students are trained interdisciplinary: classical statistical methods, programming, database systems and methods of machine learning form the methodological framework. This is supplemented by practical courses, e.g. in the areas of statistical consulting and business analytics, research-related events such as a research colloquium and courses dealing with ethical, legal and social impacts.

The following is a detailed description of the structure of the Master's programme in Data Science:

Current information on the Master's programme in Data Sciences can also be found on the university's information pages. There you will find the subject specific regulations (here in English) and the courses offered in the eKVV under the heading 'Navigation'. Further information can be found in the module list.

The four-semester Master's programme can only be taken up in the winter semester. It is divided into a socket phase and a profile phase. Due to the interdisciplinary orientation of the course of studies and the associated, differently acquired first university degrees of the students, the socket phase is composed of differently oriented introductory modules. Under certain conditions, credit points can be earned for internships. The students write their master thesis on a topic in the field of data science. Graduates are awarded the title of Master of Science (M.Sc.).

Structure of the socket phase

Due to the interdisciplinary orientation of the degree programme and the different competences of beginning students associated with it, the socket phase (variant 1 and variant 2) is made up of differently oriented introductory modules. Variant 1 is aimed at students with a Bachelor's degree in the field of economics and statistics or comparable courses of study. Variant 2  is generally aimed at students with a bachelor's degree in computer science or comparable courses of study.

Structure of the profile phase

In the profile phase, all students deal intensively with basic statistical and information technology methods and deepen their knowledge in specific areas, depending on their interests, in order to acquire a versatile spectrum of methods of statistical and information technology methods and on the other hand to adopt the special perspectives of the individual application areas.

Studies abroad can be easily integrated into the Master's programme in the compulsory optional part II and/or III by prior arrangement (e.g. through a Learning Agreement).

The students write their master thesis on a topic in the field of data science.

The profile phase is divided as follows for both variants:

Literature recommendations for R and Python

The following literature can be helpful in the preparation of your studies:

• Verzani, John. (2014). Using R for introductory statistics. The R Series (2. ed.). Boca Raton, Fla. [u.a.]: CRC Press, Taylor & Francis.
• Verzani, John. (2002). “simpleR– Using R for Introductory Statistics.” http://www.math.csi.cuny.edu/Statistics/R/simpleR.
• Toomey, Dan. (2017). Jupyter for data science. Birmingham ; Mumbai: Packt.
• VanderPlas, Jake. (2016). Python data science handbook (First edition.). Beijing; Boston; Farnham; Sebastopol; Tokyo: O’Reilly.