Justification of the need for master's degree with training in “Data Science” programme.
The main goal of the programme is to train analysts to meet the new requirements of the labor market: the ability to work in the field of analysis of multidimensional data of complex structure, including large and textual data, as well as private and personal data. The graduates will be able to systematically approach the problems related to the methodology of processing different types and forms of data (including big data), ordering access to data warehouses, restructuring the storage structure, the efficiency of processing processes.
Graduates of the Master's programme can make a practical or research-oriented career in the following areas:
- Research in computer science, big data, mathematical modelling, and artificial intelligence
- Analysis of data coming from complex systems in high technology companies, industrial enterprises, consulting firms, associations and foundations, public administration and governmental bodies
- Expertise on methodology, methods, tasks, management and analysis of big data
Management of teams in analytical, research and management departments of organizations of all forms of ownership
The graduates of the programme are employed in international and leading Russian organizations:
- IT corporations (Yandex, Google, IBS, etc.) and mobile operators
- Research centres, universities and institutes (National Research University HSE, LORIA, TU-Dresden)
- Consulting companies (PWC, E&Y)
- In the Bank of Russia and other commercial banks (Sberbank, VTB24)
The key feature of the program is the support of graduates' participation in the activities of IT-companies and IT-startups (like Datadvance, Visillect), as well as close cooperation with leading research and educational centers: IITP RAS, FRS “Informatics and Management” RAS, Skolkovo Institute of Science and Technology and Yandex Data Analysis School.
Graduates of the program will acquire skills and competences in demand on the leading online-platforms, including methods and tools for processing large volumes of data (Big Data), data preprocessing (Extract-Transform-Load), data mining (Data Mining), knowledge extraction (Knowledge Discovery), creating search engines (Search Engines), social network analysis (Social Network Analysis), algorithm scaling (Hadoop and Map-Reduce technologies), and financial time series forecasting.