Учебный план программы обучения "Data Science"

The extraction of information from data is a genuinely interdisciplinary process: The collection of data and the communication of the results of the analyses need to be linked to the domain from which the data originate. The processing and analysis of the data requires a combination of computer algorithms and statistical methodology. With the Master‘s programme established on the initiative of the Centre for Statistics (ZeSt) and the Faculty of Technology this interaction is put into practice. The aim of the programme is to enable graduates to comprehensively supervise a data-supported decision. The English-instructed programme is supported by the Faculty of Business Administration and Economics and the Faculty of Technology. The programme is addressed to Bachelor graduates interested in statistics and information technology who would like to deepen and broaden their knowledge.

The Master’s degree (120 credit points) deepens the competences and knowledge acquired in the Bachelor’s degree and serves develop the thematic focus. The Master’s programme Data Science concludes with the Master of Science (M. Sc.).

Students receive interdisciplinary training: classical statistical methods, programming, database systems and methods of the machine learning form the basic methodological framework. This is supplemented by practice-related courses, e.g. in the areas of statistical consulting and business analytics, research-related courses and courses such as a research colloquium as well as events dealing with ethical, legal and social implications. The programme thus gives interested students the opportunity to consolidate and deepen their knowledge in the field of statistics and information technology at a demanding level.

The Master’s degree programme (taught completely in English) is divided into a socket phase and a profile phase. Due to the interdisciplinary orientation of the degree programme and the different competences of beginning students associated with it, the socket phase is made up of differently oriented introductory modules. In the following 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. A research colloquium and a reading course provide students with a general overview of the current state of research in the field of data science. The multi-layered curriculum is supplemented by practical courses, e.g. in the areas of statistical consulting and business analytics. By prior arrangement, credits provided abroad can be easily integrated into the Master‘s degree programme.

Факультет

Методика оценки заявок

The documents submitted will be assessed according to points using the following criteria. Any further acquired knowledge and qualifications (paragraph 5 c) can be taken into account if this is intended to compensate for missing content in terms of the above criteria.

Criteria score

• Knowledge in the field of computer science depending on the scope (5 ECTS = 1 point): 0 - 5
• Knowledge of statistical methods depending on the scope (5 ECTS = 1 point): 0 - 5
• Knowledge in the area of ​​mathematics depending on the scope (5 ECTS = 1 point): 0 - 5
• Final grade of the qualified degree: 1.0 - 1.5 15
• Final grade of the qualified degree: 1.6 - 2.0 13
• Final grade of the qualified degree: 2.1 - 2.5 11
• Final grade of the qualified degree: 2.6 - 3.0 9
• Final grade of the qualified degree: 3.1 - 3.5 7
• Final grade of the qualified degree: 3.6 - 4.0 4

Grand total 4 - 30

Instead of a final grade, a preliminary final grade can be accepted. The decision on this lies with the according to § 22 MPO fw. responsible body that also regulates the further procedure. If provisional final grades are generally accepted in a procedure and there is no provisional final grade in individual cases, the grade 4.0 is calculated.

Applicants are granted access who have a previous qualified degree (Paragraph 1) and language skills (Paragraph 2) and receive at least 18 points according to the criteria in Paragraph 6. Applicants who do not have a previous qualified degree (Paragraph 1) and language skills (Paragraph 2) and / or who achieve less than 18 points according to the criteria in Paragraph 6 are not admitted.

If the number of applicants receives access exceeds the number of available places, the study places will be allocated in the order of the number of points achieved in the procedure. In the event of a tie, the (preliminary) final grade of the degree qualifying for the master’s degree is decisive. If there is no clear ranking to be made,
decides the lot.

Полезные ссылки по программе обучения "Data Science"

• Сайт программы: https://ekvv.uni-bielefeld.de/sinfo/publ/variante/128265757#nc
• Брошюра / инфо-материалы: https://ekvv.uni-bielefeld.de/sinfo/publ/master-as/datascience/flyer
• Учебный план / модули: https://ekvv.uni-bielefeld.de/sinfo/publ/variante/128265757?m