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 through the use of software tools. Student performance is assessed by written examinations, tests, coursework, and a dissertation.
Competences and skills
This interdisciplinary programme is built around four main groups of competences:
1. Mathematics and technical knowledge and skills in the exploration, modeling, analysis and use of the latest Big Data tools and techniques.
2. Understanding business, the connection between business and IT, how to enable enterprises to be managed more effectively by using new Big Data technologies, value chains, and implementation.
3. Management skills in Big Data systems implementation and Big Data services.
4. Research skills in analytics and optimization, focusing on stochastic optimization, predictive modeling, forecasting, data mining, business analysis, marketing analytics and others
An important advantage of this set is the resulting synergetic effect of economic, technical, and managerial skills. This enables our students to identify and evaluate the possibility of using Big Data in the appropriate business context, to justify the benefits of this technology, to develop the architecture for Big Data systems and implement it them into existing enterprise architectures.
The programmes curriculum provides a method for the side-by-side formation of the competences of the four groups, based on interdisciplinary project work.
The programme provides students with a knowledge and understanding of the fundamental principles and technological components of BigData, preparing them for careers in scientific research or within companies.
The programme is supervised by the international Scientific Council, which includes representatives of universities with leading Big Data research labs or educational programmes, as well as representatives of companies, that providing Big Data products and technologies.
Technical and methodological support
More information about Big Data professionals you may find, following the links below: