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Master's degree programme in Data Science (M.Sc.)

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.

Flyer on the study programme (to follow)

The structure of the Master's programme (taught completely in English) in Data Science is described in detail below.

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.

Socket phase 27 ECTS

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:

Profile phase 93 ECTS

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: Two 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 (FsB) 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.

Requirements and application

In order to gain access to the Master's programme, a successful completion (usually a Bachelor's degree) of a qualified previous programme of study with at least a six-semester standard period of study must be proven. Qualified is a degree lasting at least six semesters with at least 50 LP/ECTS in computer science, statistics and/or mathematics, with at least 10 ECTS each in mathematics (linear algebra, analysis) and fundamentals of computer science and 5 ECTS in statistics with formal methodological content. Any other acquired knowledge and qualifications can be taken into account.

In the case of fulfilment of these requirements, the submitted documents are evaluated according to points (see subject specific regulations (FsB) No. 2 (6)), taking into account certain criteria (specialist knowledge, (preliminary) final grade). At least 18 of the maximum 30 points from this evaluation are required for access (see FsB No. 2 (7)). For the proof of the listed knowledge, the attended courses are examined by a selection committee. A special form is provided for the compilation of this knowledge. Self-acquired knowledge and subject-specific internships cannot be taken into account in the evaluation. The selection committee decides whether or to what extent the courses attended will be evaluated. Therefore, no binding information can be given in advance. The Academic Counselling Service (datascience@uni-bielefeld.de) can answer general questions about the evaluation.

Admission also requires that the applicant has a proven knowledge of the English language. Proof is deemed to have been provided if the applicant has obtained his or her study qualification or degree qualifying for a profession from an English-speaking institution or if he or she has a language certificate (in particular TOEFL, telc, IELTS, UNIcert, Cambridge Certificate) generally recognised by German universities, which proves at least a language level of level B2 of the European Framework of Reference for Languages, or a comparable certificate. German language skills are beneficial for studying, but do not have to be proven.

Further prerequisites for admission are successful participation in a written application procedure in which the suitability for the study programme is determined. The application documents must be submitted in due time via the online application portal of Bielefeld University.

Please note that, in addition to the usual application documents such as a diploma from a previous degree and the associated documents, a complete list (including credits (ECTS)) of the achievements and qualifications in the fields of computer science, statistics and mathematics proven in the previous degree or otherwise must be attached to the application documents. A special form is provided here.

The Master's programme can only be taken up in the winter semester. The application period for each year begins on 1.6. and ends on 15.7.

Further information on admission requirements and the admission procedure can be found in the subject specific regulations.

Doctorate

The doctorate is particularly relevant for students who are aiming for an academic career after graduating with a Master's degree. This serves the consistent further development of innovative research and is composed of an independent scientific research work (dissertation) and an oral examination (disputation). The Faculty of Economics offers optimal conditions for this.

General information can be found at: www.uni-bielefeld.de/nachwuchs/promovieren

Contact / Student counselling

Dr. Basil Ell
Academic advisor

Telephone: 0521/106-2951
Room: CITEC 2-311

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

Office hours: by appointment

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