DATA
SCIENCE
MASTER PROGRAM
/Master of Science
in Mathematics and Computer Science/
Data scientists are going to be among the most demanded specialists in the hi-tech market. The purpose of our program is to meet this demand and to equip the most talented young scientists with a high-level knowledge of machine learning, deep learning, computer vision, industrial data analytics, natural language processing, mathematical modelling and other important areas of modern data science.

Skoltech is created in collaboration with MIT
Skoltech is a new global research university created in collaboration with MIT. The new campus, designed by Herzog de Meuron, is home to 40 world-class labs with best-in-class equipment, globally renowned professors and students from 40+ countries.
Mode, duration:
2 years (full-time)
Language:
English
Fees:
no tuition fees for those who successfully pass the selection
Student pack:
monthly stipend (40 000 RUB), medical insurance
Apply:
The application for 2020 is open
Education
The MSc program is 2 years long: the first year is to strengthen your theoretical background, and the second year is to focus on research. Students have the freedom to choose courses and extracurricular activities to shape their individual trajectory, acquire soft skills, and gain entrepreneurial skills to prepare for job placement.
Lectures and practical classes conducted by world-renowned professors and experts.
Students' individual research projects carried out at Skoltech laboratories.
An 8-week summer industry immersion program at leading companies turning knowledge and skills into action.
Courses on entrepreneurship and innovation that provide skills, as well as knowledge, to commercialize ideas and research findings.
+ Academic mobility at leading global universities (on a competitive basis)
IN BRIEF
CURRICULUM
RESEARCH
FUTURE CAREER
ADMISSIONS
A successful graduate of the program will know:
Mathematical and algorithmic foundations of Data Science
Main methodological aspects of both, scientific research and application development in Data Science
State of the art techniques of machine learning and related areas
Track
Machine Learning and Artificial Intelligence (MLAI)
Machine learning techniques are at the forefront of the modern data science and artificial intelligence. The curriculum of the program contains a balanced combination of topics developed very recently together with in-depth teaching of mathematical foundations, such as advanced linear algebra, optimization, high-dimensional statistics, etc.

This track is also available in network form with Moscow Institute of Physics and Technology.

A successful graduate of this track will be able to:
  • understand and formulate complex real-world tasks as data analysis problems
  • contribute to the development of the next-generation machine learning software competitive with or superior to the existing examples of software in critical and emerging application fields
  • apply relevant software tools, algorithms, data models, and computational environments for solution of real-world problems
Track
Data-Intensive Mathematical Modelling and Simulations (DIMMS)
This track aims at education of new generation of computational scientists and engineers, able to combine first principle and data-driven approaches in mathematical modeling of natural, industrial and social phenomena. The curriculum carefully balances advanced computing, machine learning, and computational physics that enables the implementation of large scale models in modern computational environments.

A successful graduate of this track will be able to:

  • construct mathematical models of industrial processes, natural, and social phenomena based on fundamental principles and precedent data
  • contribute to development of efficient algorithms and codes for computationally demanding, data-intensive modeling and simulations
  • apply relevant computational approaches, data structures, hardware and software to complex real-world problems
Track
Quantum Information Processing (QIP)
The track trains students in the contemporary theory and foundations of quantum-enhanced computing, quantum communications, and quantum information science. This multidisciplinary program bridges the gap between theory and practice when it comes to near-term and existing quantum enhanced technology, including quantum enhanced simulators being built in Russia and abroad.

The track is self-contained and is accessible for students of all backgrounds across the mathematical and physical sciences. It is based on several core computer science techniques and contains segments on proof theory, formal logic, complexity science, the mathematical theory of computation and some techniques from quantum physics. The concepts of the track often reference ideas and concepts from quantum theory and the theory of statistical mechanics and utilises machine learning and tensor network methods.

The track consists of a research project specific to the field of quantum-enhanced technology, and the electives span across both data science, theoretical physics, and applied mathematics.

A successful graduate will be able to:
  • Solve challenging academic research problems at the theoretical foundations of quantum information and quantum enhanced computation
  • Utilise quantum effects for the enhancement of computational tasks such as machine learning, optimisation and the simulation of physical systems
  • Perform computer based classical simulations of quantum systems, including HPC simulations of quantum simulators and quantum computers
Track
Statistical Learning Theory (SLT)
(in network form with Higher School of Economics)
Statistical Learning Theory is at the cutting edge of various disciplines of mathematics and computer science. It is one of the most dynamic areas of modern science, encompassing mathematical statistics, machine learning, optimization, and information and complexity theory. From the start of the program, students collaborate in thematic working groups and actively participate in research, learning from Higher School of Economics and Skoltech scientists as well as leading global specialists in statistics, optimization and machine learning.

A successful graduate of this track will:
  • possess active knowledge of modern methods and approaches in statistical learning, including mathematical statistics, stochastic processes, convex optimization
  • be able to apply and further develop such methods for solving complex practically motivated problems of data analysis
Program structure:
The 2-year program comprises of compulsory and recommended elective courses on the most important topics, a wide set of elective courses (depending on the research and professional needs of the student), components of entrepreneurship and innovation, research activity and 8 weeks of industry immersion.

MLAI
Track "Machine Learning
and Artificial Intelligence"

* also available in network form with MIPT
DIMMS
Track "Data-Intensive Mathematical Modelling and Simulations"
QIP
Track "Quantum Information Processing"
SLT
Track "Statistical Learning Theory"
* (in network form with HSE)

MLAI
DIMMS
QIP
SLT

Coursework (36 credits)
Introduction to Artificial Intelligence
Computational Imaging
Introduction to Computer Vision
Statistical Natural Language Processing
Introduction to Digital Agro
Omics Technologies
Geometric Computer Vision
Machine Learning in Chemoinformatics
Soft Matter in Practice
Hybrid Photonics Computing
Advanced Solid-State Physics

Elective courses (24 credits)

Entrepreneurship and Innovation (12 credits)
Technology Entrepreneurship: Foundation
Technology Entrepreneurship: Advanced
Product Innovation: User-Centered & Iterative Design Process
Technology Planning and Roadmapping: Foundation
Technology Planning and Roadmapping: Advanced
Technological Innovations: from Research Results to Commercial Product

Research and MSс thesis project (36 credits)

Industry Immersion (12 credits)
Programs in network form with:
  • Moscow Institute of Physics and Technology
  • Higher School of Economics (National Research University)
Industrial Partners:
  • Sberbank
  • Yandex
  • RusAgro
  • VisionLabs
  • Datadvance
  • ScanEx
  • Geoscan
  • Gazpromneft
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 Problem
Our graduates shape their own futures by choosing from a variety of career opportunities in industry, science and business:
Industry
Landing specialist positions such as Data Analyst, Data Scientist, Industrial Research Scientist, Consultant in various industry sectors (IT, Finance, Telecom and others).
Science
Landing PhD positions and continuing research at leading Russian and international research bodies.
Startup
Starting a business on their own or through the Skolkovo innovation ecosystem with its extensive pool of experts, consultants and investors.
Entry requirements:
Knowledge and skills:
Calculus, Differential Equations, Linear algebra, Probability theory and mathematical statistics, Discrete mathematics (including graph theory and basic algorithms), Programming.
Education:
IT related bachelor's degree, or its equivalent in Mathematics, Computer Science, Information and Communication Technology, Applied Physics or other technical areas.
English Language:
If your education has not been conducted in English, you will be expected to demonstrate evidence of an adequate level of English proficiency.
APPLICATION PACK:
  • Your CV (ENG)
  • Motivation letter (ENG)
  • 2 recommendation letters
  • Diploma or transcript
  • Your certificates and awards, achievements and other materials for portfolio
Selection process:
1
Registration
Fill out the registration form to start the application process.
2
Submit your application
Familiarize yourself with the list of required supporting documents, upload them into the application system as they become ready and submit your application.
3
Online testing
Every candidate must take an online profile test. You will be notified by email about the specific date and time of your test.
4
In-person interviews
The final admissions stage takes place in Moscow. You have to pass the TOEFL ITP exam on site, or present a valid TOEFL certificate and pass an in-person interview. Extra written examinations may be required for certain programs during this time (you will be notified in advance).
Exam samples:
Keep in mind that the structure of exam can be changed
following the decision of program committee.
Faculty
The program has a globally renowned faculty with international experience
and a broad network of collaborations:
Program Coordinator:
Maxim Panov
Assistant Professor
Alexey Artemov
Research Scientist
Assistant Professor
Professor
Professor of the Practice
Associate Professor
Associate Professor
Professor
Assistant Professor
Assistant Professor
Associate Professor
Associate Professor
Professor
Research Scientist
Assistant Professor
Leading Research Scientist
Associate Professor
Assistant Professor
Professor
Associate Professor
Research Scientist
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Associate Professor
Assistant Professor
Associate Professor
Professor
Associate Professor
Yury Yanovich
Research Scientist
Associate Professor
Adjunct Professor
Application
The application period for 2020 is open.
Contact
e-mail: admissions@skoltech.ru
phone: +7 (495) 280-1481_ext.3481
address: 30с1 Bolshoi boulevard, Skolkovo, 121205, Russian Federation, Room E-R3-2030

We are more than happy to meet visitors Monday to Friday from 9:00 to 18:00. Please arrange a visit 48 hours in advance by contacting admissions@skoltech.ru