Programme Overview
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 2 specializations and a full-time English-taught track (120 credits):
- English-taught track
Basic Courses:
Modern Methods of Decision Making
Ordered Sets in Data Analysis
Linear Algebra for Data Science
Machine Learning and Data Mining
Elective Courses:
Medical Informatics
- Intelligent Systems and Structural Analysis
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
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