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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.

 

Concept of the Master's degree programme

The four-semester Master's programme is aimed at Bachelor's graduates interested in statistics and information technology who wish to deepen and expand the knowledge they have previously acquired in a relevant Bachelor's programme. The educational background should be sufficiently methodical, proven, for example, by a Bachelor (or Master) degree in statistics, computer science, (economic) mathematics, economics or a Bachelor degree with a quantitative profile in mathematics, computer science and statistics.

Students should have a strong 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.

 

Educational goal

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

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

 

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.

 

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).

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.

* 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).

 

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 Master's programme is located 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 chairs of the following members of the Centre for Statistics are participating in the Master's programme Data Science:

  • Prof. Dr. Dietmar Bauer (Faculty of Business Administration and Economics)
  • Prof. Dr. Philipp Cimiano (Faculty of Technology)
  • Prof.'in Dr. Christiane Fuchs (Faculty of Business Administration and Economics)
  • Prof. Dr. Friedrich Götze (Faculty of Mathematics)
  • Prof.'in Dr. Barbara Hammer (Faculty of Technology)
  • Prof. Dr. Roland Langrock (Faculty of Business Administration and Economics, Spokesman of the Centre for Statistics)
  • apl. Prof. Dr. Hans Peter Wolf (Faculty of Business Administration and Economics)

 

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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