About the programme
The programme is focused on the value aspect of Big Data for large enterprises and the implementation of Big Data technology in the enterprise.
The value is one of the important characteristics that distinguish the Big Data as a new phenomenon. It is directly related to the economic impact that Big Data technology provides to users. Big Data storage in the field of financial services, telecommunications, retail and Government Administration were used to solve various business problems for many years.
Big Data technology can reduce the cost of IT infrastructure and software, reduce labour costs through more efficient methods of data integration, management, analysis and reaching a decision; increase revenue and profit by new or better ways of doing business.
This transforms the role of systems for the collection, compilation and use of data makes it the basis of the enterprise's business strategy. This requires professionals with the new skills.
The programme provides students with a knowledge and understanding of the fundamental principles and the technological component of Big Data, preparing them for a career within companies or in scientific research.
The program consists of core courses, optional courses, term paper (first year), research seminar and master's thesis (second year).
Bridging courses in the first term allow you to fill knowledge gaps and to take courses, regardless of your previous degree discipline.
Bridging courses (dependent on the student):
- Data Bases
- Enterprise Architecture
- Data Analysis
- System Analysis & Organization Design 4 credits
- Economic and Mathematic Modeling 3 credits
- Enterprise Architecture Modelling 3 credits
- Advanced Data Analysis & Big Data for Business Intelligence 5 credits
- Big Data Systems Development and Implementation 5 credits
Elective courses (5 out of 7)
- Knowledge Discovery in Data at Scale Technologies 3 credits
- Cloud computing 3 credits
- Data Visualization 4 credits
- Advanced Data Management 3 credits
- Big Data Collection, Storage&Processing in Heterogeneous Distributed Computer Networks 5 credits
- Natural Language Processing 3 credits
- Applied Machine Learning 4 credits
Elective courses (3 out of 5)
- Creating and Managing Enterprise Information Assets 6 credits
- Predictive Modeling 6 credits
- Big Data Based Marketing Analytics 6 credits
Big Data Based Risk Analytics 6 creditData-Driven
- en Process Control 6 credits
Optional course from the University Set 3 credits
Scientific and Research Internship 12 credit
Term Paper 5 credits
Scientific and Research Seminar 17 credits
Master’s thesis 24 credits
Teaching and Learning
The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by individual and group project work with using of software tools. Student performance is assessed by written examinations, tests, coursework and the dissertation.
Competences and skills
The proposed programme is interdisciplinary. The course forms the four groups of competences.
- Mathematics and technical knowledge and skills in an area of exploration, modelling, analyzing and using the latest Big Data tools and techniques.
- The understanding of business, the connection between business and IT, the understanding, how to enable enterprise to be managed more effectively by using new Big Data technologies, value chains, produced by their implementation.
- Management skills in the area of Big Data systems implementation, Big Data services.
- Research skills in an area of analytics and optimization skills, focused on stochastic optimization, predictive modelling, forecasting, data mining, business analysis, marketing analytics and others.
An important advantage of this competence set is the resulting synergy effect of economic, technical and managerial skills. This allows you to identify and evaluate the possibility of using Big Data in the appropriate business context, to justify the benefits of this technology; to develop an architecture of Big Data system and implement it into existing enterprise architecture.
Programme curricula provides a method for side-by-side formation of the competences of the four groups, based on an interdisciplinary project work.
The programme provides students with a knowledge and understanding of the fundamental principles and the technological component of Big Data, preparing them for a career in scientific research or within companies.
The programme is supervised by the international Scientific Council, which includes the representatives of universities, in which there are research labs or educational programmes on Big Data and the representatives of companies, providing BigData products and technologies.
Technical and methodological support
Students will use IBM software products for data analysis and modelling.
University of Munster (Germany)
Selection process is based on the portfolio competition. The candidates should provide the following documents:
- A scan of the first page of your passport.
- Bachelor’s (Specialist’s or Master’s) diploma and official transcripts of previous educational studies. (if you have not yet received your Bachelor’s diploma, please include an official copy of your most recent academic transcript) A good background in discrete mathematics, linear algebra, mathematical analysis, probability calculus, economics, software engineering, databases is needed. The Admission Committee takes into consideration the number of hours and final assessments.
- Letter of motivation (describing your reasons for applying in the context of your long-term career goals and background, 500 words) The letter should describe the candidate’s reasons for applying to this Master’s Programme, in the context of the candidate’s long-term career goals and background.
- Project and practical experience in IT field The candidates should provide copies of employment records and/or employment agreements. If the candidate worked in a university research laboratory, confirmation from Study Office (Curriculum Support Office) is required. If the candidate participated in an IT project in a private company, confirmation from Project Leader is necessary.
- Resume/CV (including information about your education, professional, and research experience, as well as language proficiency and other skills).
- Exam results confirming language proficiency (optional).
Program taught in: