Business Analytics and Big Data Systems

The programme provides a unique set of interdisciplinary competencies, including mathematics and technical knowledge and skills in the exploration, assessment, analysis and use of big data tools and techniques. These competencies help in understanding the architecture of businesses and the connection between business and IT, thereby enabling enterprises to be managed more effectively by using new big data technologies. The programme also aims to build management skills in big data systems implementation and big data services, as well as research skills in big data analytics, predictive modelling, forecasting, data mining, business analysis, and mathematical modelling in economics.

In the QS World University Rankings by Program: Business Masters, the HSE programme is positioned as a programme in business analytics (QS Business Masters Rankings: Business Analytics – MSc in Business Analytics, specializations: Operation analytics); it is the only master’s programme offered at a Russian university to make this prestigious ranking.

The programme’s leading reputation is supported by an English-language learning environment, international integration, practical orientation of courses, as well as prestige and competitiveness of the profession for which students are trained.

On-site learning is offered at the Shabolovka HSE building (26 Shabolovka Ulitsa, Moscow, Russia). Classes take place in the evening (from 6:10 to 9:00 pm) on workdays and during the day on Saturdays, which allows our students to combine work and studies effectively. Instruction is entirely in English.

Courses are delivered by leading HSE teachers and visiting expert professionals. All teachers have experience successfully teaching in English.

The curriculum is composed of core subjects and electives that can be chosen based on a student’s individual starting competencies and satisfaction of course prerequisites. Subjects from other master’s programmes (English-taught) can also be included in the individual curriculum.

Core Courses:

• Economic and Mathematic Modelling
• Enterprise Architecture Perfection
• Methods and Tools for the Intellectual Analysis of Big Data
• Strategic Innovation Management
• System Analysis and Organization Design

Electives

• Advanced Data Management
• Applied Blockchain in the Modern Enterprise Architecture
• Applied Machine Learning
• Big Data Based Marketing Analytics
• Big Data Collection, Storage & Processing in Heterogeneous Distributed Computer Networks
• Big Data Systems Development and Implementation
• Cloud Technologies
• Data Analytics and Visualization for Business
• Digital Platforms and Ecosystems of Modern Business
• Knowledge Management
• Leadership and Project Team Management
• Manufacturing Data Collection and Analytics
• Neural Networks and Deep Learning
• Predictive Modelling
• Theoretical Basics of Distributed Information Processing in Big Data Systems

The curriculum also includes the following bridging courses: Analysis for Business Systems, Data Science for Business Innovation, and Python for Data Science and AI.

Finally, a major focus of the programme is the development of research competencies and practical professional skills.

The programme is offered with support and close collaboration from corporate partners of the Graduate School of Business and companies that develop products for big data analytics, including Huawei, SAP, Oracle and LANIT.

Cooperation with partner companies provides students with access to the latest software products via cloud services, as well as internship opportunities at Russian and international research laboratories.

Students of the master’s programme in Business Analytics and Big Data Systems can apply for academic mobility programmes:

1. Double-degree programmes (long-term education programmes under which students receive degrees from several universities) - University of Passau (Germany)
2. Short-term exchange programmes (usually, 1 module or 1 semester at a foreign university).

Graduates of the programme are cross-functional professionals who are able to effectively use next-generation digital technologies to effectively mine a variety of data and to use them to develop new products and services. Graduates also understand the technical aspects of big data systems; know how to use technical tools as well as methods to collect, process and analyse data; and have the leadership and team management skills required to manage changes in organizations.

Graduates of our programme are able to become organizers and leaders of companies’ digital transformations and become advocates of data-driven culture and management strategies. As such, they enjoy strong demand in Russian and international organizations that practice big data storage and use. They are also well positioned for opportunities in organizations that have an interest in effective digital transformation of their business processes on the basis of big data technologies.

Our graduates also have the ability to continue their studies at the doctoral level at HSE University, as well as in PhD programmes of international universities.

The application for the programme in Business Analytics and Big Data Systems can be completed through the portfolio competition and after a successful English language exam, which includes a test and a listening assignment.

Students are enrolled in both state-funded and fee-paying slots.

The portfolio consists of:

1. A copy of a student’s diploma and official transcript, or an official document, issued by a higher education institution confirming programme completion or indicating the number of degree credits earned to date
2. Documents confirming the applicant’s individual achievements
3. Documents confirming the applicant’s practical experience in the programme field
4. Letter of motivation (in English)
5. Essay on the subject of “Multimodal models of artificial intelligence as the basis of future services” (in English)
6. Oral interview (in English)

More information about the application for the programme is available on the Graduate Admissions page.