• ‘Big Data Analysis and Machine Learning in Economics and Finance’

    • Full-time
    • 2 years
    • RU
    • 350 000 RUB
  • Entrance exams

    • Mathematics and Computer Science
    • Foreign language (English, Chinese, German, French, Spanish)
  • Accreditation and Partners

Vladimir Igorevich Solovyov

The program is aimed at training specialists in big data analysis and machine learning in economics and finance. 

Graduates of this program have theoretical knowledge of statistical analysis, including methods of parametric and non-parametric statistics, regression and cluster analysis. They know the theory of complex networks and reference systems, analysis of text information and image processing. They are aware of the modern technologies and tools of data source search, collection, visualisation and processing of structured and unstructured data. We teach them how to build and analyse models of machine learning, to identify patterns in data, and to apply the obtained results while solving practical problems in the field of economics and finance. 

Our graduates are ready to use intellectual technologies in risk management, for example, to predict the dynamics of financial instruments prices, to identify cases of fraud with credit cards and insurance products, money laundering, tax evasion, and etc. 

Moreover, our graduates know how to prepare personalised and behaviour-based products and services, for instance, automobile insurance policies, which take into account characteristics of the driving style. They can create credit scoring systems or advisory services on acquiring financial instruments based on analysis not only of quantitative data of the assets’ pricing dynamics, but also unstructured information, including news in the Internet, customer relationship history, their mood, their behaviour in the internet and social networks. 

Main disciplines

  • Methodology for Data Source Search and Data Preparation for Analysis

    Students will learn how to use methods of finding data sources and preparing data for further analysis.

  • Data Visualisation Methods

    Students will acquire skills of practical application of modern computer software for visualisation of digital financial and economic information and interpretation of the results obtained.

  • Machine Learning Model Building and Assessment

    Students will master the methodology of construction and estimation of models of machine learning and will acquire skills of building models within the framework of applications in the financial and economic sphere.

  • Predictive Analytics and Big Data

    Students will acquire skills of using big data analysis to solve applied financial and economic programs.

  • Recommender Systems

    Students study methodology of constructing advisory systems and their evaluation. They will also acquire skills of building advisory systems, which are applied in social and financial spheres.

  • Modern Neural Network Technology

    Students will learn the main aspects of the way artificial neural networks function, study various models of neural networks and methods of their training, and acquire skills in applying neural networks for applications.


Our graduates know the modern mathematical theory of big data analysis and machine learning and modern information technologies of data analysis. They are able to extract valuable information form structured and unstructured data and have experience in solving real practical tasks of analysing open data and information from social networks, developing customer segmentation and credit scoring systems, and creating advisory and behavioral services. 

Specialists, who graduated from this program, are employed in banks, investment, insurance, telecommunications, trading, production companies, organisations of various types of ownership, industry and business. They are engaged in developing and operating information systems, intellectual products and services, which are based on technologies of artificial intelligence and scientific advances in smart techniques for big data analysis and machine learning.