Degree :
 Master
Subject area:
 Mathematical and natural sciences
Subject:
 01.00.00 Mathematics and mechanics
Location:
 Moscow, Russia
Duration:
 2 years
Study type:
 Full time
Language of instruction:
 English
Start date:
 01-09-2019
Application deadline:
 30-06-2019
School:
 Faculty of Computer Science
Tuition fees per year:
 USD 5100
Note: For CIS students tution fee is 330000 per year
20 Myasnitskaya Street, Moscow, Russia, 101000, HSE University

Faculty of Computer Science

Course Summary

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 modeling 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 
Data and Service Engineering for Automating Business Processes 

  • 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

Admission Requirements

Students are selected on the basis of their portfolio, which includes required and optional components. The Admission Committee may invite the applicant for an interview (by Skype). 

 

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