Master in Data Science

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 program includes training in the fields of computational models, mathematical modeling and forecasting, computer architecture, advanced programming techniques, as well as data storage and retrieval. On the strength of its multidisciplinary design, this program may serve as a backbone that is of interest to graduates of numerous faculties, as well as to staff members at research centers. Graduates of the program 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.

Mati Mango / pexels

Program Overview

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

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

The program 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 a computer realization of these methods. The 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 program 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

Graduates of the program will acquire skills and competencies in demand on the leading online platforms, including methods and tools for processing large volumes of data (Big Data), data preprocessing (Extract-Transform-Load), data mining (Data Mining), knowledge extraction (Knowledge Discovery), creating search engines (Search Engines), social network analysis (Social Network Analysis), algorithm scaling (Hadoop and Map-Reduce technologies), and financial time series forecasting.