Data Analytics for Business and Economics

Cross-Functional Competencies

The programme’s courses cover a wide range of competencies in strategic management and business economics, as well as in big data analytics, quantitative methods, and machine learning.

An Applied Curriculum

The curriculum is oriented around the knowledge and skills most sought-after among employers in real workplaces.

Experienced Teaching Staff

Courses are taught by academic and business experts.

The main focus of the programme is data analytics. In order to build new business models and make informed management decisions, students master a wide range of competencies, from advanced analytics methods (Google IQ, Yandex Metrica Expert) to data visualisation tools. The programme also includes courses on statistical data processing and applied machine learning methods.

The programme has three applied tracks:

1. Customer Analytics

This track focuses on the following topics:

• How to optimise marketing spending across different channels
• How to predict and prevent customer churn
• Which customers generate the most revenue
• Which geographical areas should be targeted
• Which customers are likely to return within the next three months
• Which additional products can be offered to each customer

2. People Analytics

Students on this track gain all the necessary knowledge and analytical skills to lead corporate HR management. Courses are built upon real examples, and the lecturers of this track are leading HR experts. A practice-oriented approach enables students to familiarise themselves with the existing environment, adopt leading practices in personnel management, communicate with leading experts, and make the most of networking opportunities.

Graduates of the programme have a deep understanding of modern HR technologies and organisational culture and possess data analytics skills. This makes them indispensable in solving such issues as:

• the transition to self-directed learning
• the implementation of digital HR technologies
• adopting evidence-based solutions

3. Economic Data Analysis

This track trains research economists with extensive knowledge of modern economic theory and an advanced understanding of economic and mathematical methods (including econometrics, machine learning, and computer programs).

The goal of the track is to train business analysts capable of creating analytical models for business, researching demand, and analysing data on purchases, auctions, pricing experiments, and online customer behaviour.

Graduates have the skills and knowledge to work as analysts for major companies, commercial banks, insurance companies, investment funds and other financial institutions.

• Enter a highly paid profession with good future prospects
• Enjoy opportunities to work remotely
• Master a new profession from scratch

• Coordinate project work, linking technical specialists and administrative departments
• Help make optimal decisions by analysing data in business, science, and management
• Process large volumes of information
• Calculate key performance metrics and evaluate their significance
• Visualise data using dashboards to quickly interpret obtained results and future trends

The programme is targeted at applicants who are focused on data analytics within an organisation.

Potential candidates must have a higher education in a STEM field or in economics and/or management, as well as the motivation to work in data analytics.
Candidates can be referred to the programme by organisations interested in the continuing professional development of their staff.

Project activity is a key element of the curriculum. Projects allow students to consolidate their knowledge and skills by solving real tasks from the programme’s partners.

All students of the master’s programme undergo an internship (a business internship or an internship at a research centre devoted to market analysis and game theory, such as the International Laboratory of Game Theory and Decision Making or the Strategic Entrepreneurship Centre). Students can also spend a semester studying at leading foreign business schools partnered with the university.

The programme trains managers and economists with cross-functional competencies. By the end of the programme, students will be able to make management decisions by applying the latest tools for gathering, analysing, and visualising data in the fields marketing, economics, and HR management.

Graduates of the programme can pursue careers in data analytics and machine learning in such roles as:

1. Project manager
2. Data analyst
3. Business analyst / BI analyst
4. Marketing analyst
5. HR analyst
6. Economic analyst

Another goal of the programme is to give those interested in academia an opportunity to gain a deeper understanding of the field.
After completing their studies, graduates can choose to continue their academic career by applying to doctoral programmes in Russia or abroad.

For international applicants:

1. Competitions / Olympiads:

Graduate international applicants are welcome to take part in the various competitions organised by HSE University. Learn more and register at https://olymp.hse.ru/en/

2. International Admissions:

All applicants have the opportunity to be enrolled as a self-paid student or get a scholarship from the Government of the Russian Federation. Self-paid students may be entitled to tuition fee discounts (25% or 50% of the tuition fee per year) based on the results of entrance trials.

Detailed information on the application process for international students is available here.

If you have any questions concerning international admissions, please email us iadmission-spb@hse.ru

3. General Admission (limited eligibility)

Certain categories of international applicants are eligible to apply for a state-funded (free of fees) place or for a self-funded place at our programmes along with Russian applicants. Find more here (page in Russian).

Please note: within the general admission international applicants will compete with Russian applicants.

For Russian applicants:

Please refer to the How to apply page in Russian.