Master in Data Science

Higher School of Economics

Program Description

Master in Data Science

Higher School of Economics

In order to analyze the growing volume of data generated in all areas of today’s society, the modern IT industry is elevating the issue of Big Data. Likewise, the academic community is establishing the emerging field of Data Science. This programme includes training in the fields of computational models, mathematical modelling and forecasting, computer architecture, advanced programming techniques, as well as data storage and retrieval. On the strength of its multidisciplinary design, this programme may serve as a backbone that is of interest to graduates of numerous faculties, as well as to staff members at research centres. Graduates of the programme will be able to solve problems concerning data search, collection, storage, preparation and analysis, as well as interpretation of results in the area of specialization.

About the Programme

Data Science master’s programme includes the full-time educational track for English-speaking students which consists of a set of basic disciplines and variety of elective and optional courses in English.

The aim of the programme is to train highly-qualified experts in applied mathematics, information science and data analysis.

The programme involves an in-depth study of mathematical methods of artificial intelligence models and modern methods of data analysis, mathematical and informational modelling of complex systems as well as computer realization of these methods. Knowledge and skills of graduates from this course are in demand by Russian Federation ministries and institutions, regional administrations and large companies.

The concept and the curriculum of the specialization in Internet Data Analysis have been developed in conjunction with Yandex. This track involves the teaching of special disciplines by the Company staff members, the participation of students, postgraduates and lecturers in projects implementing tasks suggested by Yandex and related to its business operations, vocational training for students in Yandex and joint research carried out together with Yandex staff.

The programme includes 3 specializations and a full-time English-taught track (120 credits):

English-taught track

General Curriculum Contents

Bridging Courses:

  • Discrete Mathematics for Application and Algorithm Development
  • Probability Theory and Mathematical Statistics
  • Components of the Field of Study

Basic Courses:

  • Modern Methods of Data Analysis
  • Modern Methods of Decision Making
  • Network Science
  • Machine Learning and Data Mining

Elective Courses:

  • Automated Methods for Program Verification
  • Medical Informatics
  • Data Analysis in Medicine
  • Stochastic Modelling

Internet Data Analysis

Basic Courses:

  • Modern Methods of Data Analysis
  • Modern Methods of Decision Making
  • Machine Learning
  • Algorithms and Data Structures
  • Methods and Systems for Processing Big Data

Elective Courses:

  • Probabilistic and Statistical Approaches in Decision Making
  • Theory Parallel and Distributed Computations
  • Optimization in Machine Learning
  • Image and Video Analysis
  • Automatic Processing of Texts
  • Deep Learning

Intelligent Systems and Structural Analysis

Bridging Courses:

  • Discrete Mathematics for Application and Algorithm Development
  • Probability Theory and Mathematical Statistics

Basic Courses:

  • Modern Methods of Data Analysis
  • Modern Methods of Decision Making
  • Ordered Sets in Data Analysis
  • Network Science
  • Introduction to Machine Learning and Data Mining
  • Machine Learning and Data Mining

Elective Courses:

  • Computational Linguistics and Text Analysis
  • Information Theory and Combinatorial Theory of Search
  • Fundamentals of Design and Implementation of Artificial Intelligence
  • Systems Games and Decisions in Data Analysis and Modelling
  • Data Analysis in Medicine
  • Big Data Analysis
  • Deep Learning
  • Automated Methods for Program Verification
  • Medical Informatics
  • Robust Methods in Statistics
  • Decision Making and Data Analysis under Uncertainty and Ambiguity
  • Automating Business Processes using Machine Learning

Technologies of Modelling of Complex Systems

Bridging Courses:

  • Discrete Mathematics for Application and Algorithm Development
  • Probability Theory and Mathematical Statistics

Basic Courses:

  • Modern Methods of Data Analysis
  • Modern Methods of Decision Making
  • Ordered Sets in Data Analysis
  • Mathematical Foundations of Modern Telecommunications
  • Statistical Methods for Predictive Modeling
  • Geometric Methods for Predictive Modeling

Elective Courses:

  • Computational Linguistics and Text Analysis
  • Information Theory and Combinatorial Theory of Search
  • Fundamentals of Design and Implementation of Artificial Intelligence
  • Systems Games and Decisions in Data Analysis and Modelling
  • Data Analysis in Medicine
  • Big Data Analysis
  • Deep Learning
  • Automated Methods for Program Verification
  • Medical Informatics
  • Robust Methods in Statistics
  • Decision Making and Data Analysis under Uncertainty and Ambiguity
  • Automating Business Processes using Machine Learning
This school offers programs in:
  • English


Last updated November 15, 2018
Duration & Price
This course is Campus based
Start Date
Start date
Sept. 1, 2019
Duration
Duration
2 years
Full time
Price
Price
Information
Deadline
July 15, 2019
for Russian government scholarships
Locations
Russia - Moscow, Moskva
Start date : Sept. 1, 2019
Application deadline July 15, 2019
End date Request Info
Start date : Sept. 1, 2019
Application deadline July 15, 2019
for Russian government scholarships
End date Request Info
Dates
Sept. 1, 2019
Russia - Moscow, Moskva
Application deadline July 15, 2019
for Russian government scholarships
End date Request Info
Application deadline July 15, 2019
End date Request Info