Master of Science Program
The Statistical Learning Theory track of the Data Science MSc program is conducted jointly with the Higher School of Economics. This track stands at the crossroads of various disciplines of modern mathematics and computer science, including statistics, optimization, learning theory, information theory, complexity theory, as well as at the intersection of science and innovation in the field of modern information technology. Leading experts at the HSE and Skoltech jointly provide instruction in this unique research-driven program.
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|Modes and duration
Full time: 2 years
No tuition fee for applicants who pass the selection process
Master of Science in Data Science
Field of Science and Technology
02.04.01 Mathematics and Computer Science
|Language of instruction
The program is accredited by the Russian Government, certificate № 2568 from April 14, 2017. License № 2534 from February 7, 2017.
IT-related Bachelor’s degree, or it’s equivalent in mathematics, computer science, information and communication technology, applied physics or other technical areas.
- Differential equations
- Linear algebra
- Basic probability, random processes and mathematical statistics
- Discrete mathematics (including graph theory and basic algorithms)
|English language requirements
If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
Aim and objectives
The aim of the program is to prepare the technological leaders of the future. The objective of the Data Science MSc program is to bridge the gap between fundamental science and cutting edge computational techniques.
The curriculum of the program contains a balanced combination of topics developed very recently (e.g. deep learning) together with in-depth teaching of mathematical foundations (advanced linear algebra, optimization, high-dimensional statistics etc.).
Learning and professional outcomes
A successful graduate of the program will know:
- Mathematical and algorithmic foundations of data science. A balanced vision on mathematical foundations and practical tools and applied problems in data science;
- Statements of all major data analysis problems as well as the main approaches to solve them;
- State of the art techniques of data analysis and related areas. Knowledge of main classes of applied problems;
- Main methodological aspects of both scientific research and application development in data science.
A successful graduate of the program will be able to:
- Formulate/model real-world tasks as data analysis problems;
- Choose the most appropriate method to solve a particular data analysis problem;
- Apply data analysis methods in practice using modern data analysis software tools;
- Develop new methods or adapt existing methods to a particular problem;
- Implement algorithms as computer programs;
- Evaluate results of data analysis processes;
- Work with technical literature (e.g. conduct bibliographical research, read and critically analyze scientific articles, use scientific metrics and important databases);
- Present results to different audiences (specialists, users, stakeholders, etc) in an effective oral and written manner.
Career opportunities and paths
The Data Science MSc program was developed to meet the high demand for data science specialists in the growing national and international high-tech market. Graduates of the program may begin an international research career or work with a company (even during the period of study).
Data Science MSc graduates significantly enhance their employability by developing their subject-specific knowledge in the field of data science and machine learning, as well as their analytical and research skills. Students gain the opportunity to obtain early access to the national and international research and innovation landscapes and can approach international employers with confidence. In addition, the program enhances students’ soft skills, enabling students to compete effectively in the job market.
- PhD positions in academic & research institutions
- Specialist positions such as data analyst, data scientist, consultant in various economy sectors:
- Skolkovo resident companies and startups
Vice-President for Artificial Intelligence and Mathematical Modelling, Professor
- Alexander Bernstein, Professor of the Practice
- Christoph Hermann Borchers, Head of Laboratory of Omics technologies and Big data for Personal Medicine and Health, Visiting Professor
- Nikolai Brilliantov, CDISE CREI Director, Professor
- Evgeny Burnaev, Associate Professor
- Andrzej Stanislaw Cichocki, Full Professor
- Dmitry Dylov, Associate Professor
- Maxim Fedorov, Vice-President for Artificial Intelligence and Mathematical Modelling, Professor
- Gonzalo Ferrer, Assistant Professor
- Alexey Frolov, Associate Professor
- Nikolay Koshev, Assistant Professor
- Yury Kostyukevich, Assistant Professor
- Dmitry Lakontsev, Associate Professor of the Practice
- Victor Lempitsky, Associate Professor
- Evgeny Nikolaev, Full Professor
- Ivan Oseledets, Full Professor
- Pavel Osinenko, Assistant Professor
- Vladimir Palyulin, Assistant Professor
- Alexander Panchenko, Assistant Professor
- Maxim Panov, Assistant Professor
- Anh-Huy Phan, Associate Professor
- Petr Popov, Assistant Professor
- Mariia Pukalchik, Assistant Professor
- Sergey Rykovanov, Associate Professor
- Andrey Somov, Assistant Professor
- Vladimir Spokoiny, Full Professor
- Natallia Strushkevich, Assistant Professor
- Alexey Vishnyakov, Associate Professor
- Dmitry Yarotsky, Associate Professor
- Dmitry Yudin, Assistant Professor
- Alexey Zaytsev, Assistant Professor
- Denis Zorin, Adjunct Professor
From the HSE side, lectures are delivered by prominent professors including Dr. Yurii Nesterov, Dr. Denis Belomestny, Dr. Dmitry Vetrov, Dr. Andrei Sobolevski, and Dr. Quentin Paris.
Students are actively involved into research activity starting from Term 3.
Main research areas:
- Machine Learning and Deep Learning
- Industrial Analytics
- Computer Vision
- Image Processing
- High-dimensional statistics and Statistical learning
- Next Generation Multiscale Modeling
- Fast Solvers for Large Scale/High-Dimensional Problems
- Gazprom Neft
Student success stories
- Oleg Grinchuk and Aijan Ibraimova have NIPS* papers accepted that are based on their MSc thesis works.
- Alexander Anikin, Andrey Rykov, Vladislav Ishimtsev and Denis Volkhonskiy became prize winners at the International Data Science Game 2016-2017.
Apply at Skoltech and Apply at HSE