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Master's degree programme in Data Science

Due to the constant increase in data volumes and data complexity, Data Science has created a new, interdisciplinary field of work that covers a wide range of aspects of data analysis, such as handling large amounts of data, statistical modelling, visualisation, pattern recognition with machine learning methods, but also ethical and legal questions. Data Scientists are urgently needed for many socially significant developments, e.g. in the areas of intelligent vehicles or housing, artificial intelligence or social media.

The extraction of information from data is a genuinely interdisciplinary undertaking: The data collection and the communication of the results of the analyses require a link to the domain from which the data originate. The processing and analysis of the data requires an interaction of computer algorithms with statistical methods.

This interaction is put into practice with the Master's programme established on the initiative of the Centre for Statistics (ZeSt) and the Faculty of Technology.

Concept of the study programme

The four-semester Master's programme is aimed at Bachelor's graduates interested in statistics and information technology who wish to deepen and broaden the knowledge they have acquired in a relevant Bachelor's programme. The previous education should be sufficiently methodologically oriented, proven, for example, by a Bachelor's (or Master's) degree in the field of statistics, computer science, (business) mathematics, business administration, economics or a Bachelor's degree with a quantitative profile in the field of mathematics, computer science and statistics.

Students should have a pronounced interest and a basic understanding of statistics and computer science.

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.

Aim of the programme

The aim of the programme is to enable graduates to comprehensively supervise a data-based decision. This activity requires the ability to communicate even with scientists and users from other disciplines. This ability is imparted to students in a natural way through the inter-faculty training.

Graduates are awarded the title of Master of Science (M.Sc.).

Requirements and application

A prerequisite for the degree programme is participation in an application procedure, in which it is determined who is suitable for the degree programme and is granted approval. As part of this procedure, it is verified whether a first university degree qualifying for the master's program is available. This is verified by means of the degree certificate and the associated documents (transcript of records, diploma supplement, etc.). To what extent further approval requirements exist or the submission of further documents is intended (language requirements, elaboration with statements on qualification, exposé, project drafts or similar), please refer to the current subject-specific regulations (here in English) of the degree programme. There you will also find regulations on how the individual criteria are evaluated and weighted.

The application procedure takes place via the online application portal of Bielefeld University. Course start is possible in the Winter semester. You must apply online for the Master's program (usually from June 1). This is generally possible until July 15. The Student Office will inform you about the current deadlines for applications.

Comprehensive information on the application procedure can be found on the website of the Student Office.

Detailed information on the approval and admission procedure for the Master Data Science and the required application documents can be found here.

This Master's program is admission restricted (local NC). For the distribution of study places (approval procedure), the overall result of the above-mentioned approval procedure is generally used and a corresponding ranking is established. In exceptional cases, further criteria are taken into account. Information on the structure of the admission procedure can also be found in the subject-specific regulations.

Please note that there are specific regulations for applicants who have completed school or university examinations outside of Germany. More information on this topic you may find on the webpage of our Studierendensekretariat [Student Office].

Overall, only 7% of the places on the Master's degree programmes are reserved for applicants from countries that do not belong to the European Union.

Curriculum

The four-semester Master's programme with 120 credits/ECTS (ECTS=European Credit Transfer and Accumulation System) is divided into a socket phase (Sockelphase) and a profile phase (Profilphase). In the profile phase there is one compulsory area and three elective areas.

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. The following five modules are studied:

Variant 2  is generally aimed at students with a bachelor's degree in computer science or comparable courses of study. The following four modules are studied:

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. 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:

Compulsory part:

Electives I: Modules in the amount of 10 LP from the module pool "Advanced Machine Learning" are to be studied. The following modules are available:

Electives II:

Electives III: Modules in the amount of 20 LP from the module pool "Wahlpflicht Informatik" have to be studied. The following modules are available:

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

* by prior arrangement for stays at foreign universities
** Module 39-Inf-BDA is compulsory for students of variant 1 (Economic Sciences/Statistics), but optional for students of variant 2 (Computer 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.

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.

Participating Faculties and Chairs

The programme is based at the Faculty of Business Administration and Economics and is supported by the Faculty of Business Administration and Economics and the Faculty of Technology. The following members of the Centre for Statistics participate in the Master's programme Data Science:

Contact / Student counselling

Christoph Düsing
Academic advisor

Telephone: 0521/106-12143
Room: CITEC 2-044

and

Dr. Nina Westerheide
Coordinator Centre for Statistics/Academic advisor

Telephone: 0521/106-3822, -6930 (secretarial office)
Room: U3-148, V9-138 (secretarial office)

Email: datascience@uni-bielefeld.de


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