Degree :
Subject area:
 Social sciences
 38.00.00 Economics and Management
 Moscow, Russia
 2 Years
Study type:
 Full time
Language of instruction:
Start date:
Application deadline:
  Faculty of Business and Management
Tuition fees per year:
 USD 6870
Note: For CIS students tution fee is 450000 per year
20 Myasnitskaya Street, Moscow, Russia, 101000, HSE University

Faculty of Business and Management

Course Summary

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.

Scientific Council

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

Students use IBM  software products for data analysis and modeling.

Admission Requirements

Selection process is based on the portfolio competition. The candidates should provide the following documents:

1. Scan of the first page of your passport

2. 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.

3. 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.

4. 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.

5. Resume/CV (including information about your education, professional, and research experience, as well as language proficiency and other skills)

6. Exam results confirming language proficiency

IELTS score of 5 or above

TOEFL IBT (Internet Based) score of 60 or above

TOEFL РВТ (PaperBased) score of 500 or above

CAE (Certificate of Advanced English) pass

CPE (Certificate of Proficiency in English) pass

ВЕС Vantage/Higher (Business English Certificate) pass


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