DATA
SCIENCE
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 high-level knowledge and experience in machine learning, deep learning, computer vision, industrial data analytics, natural language processing, mathematical modelling and other important areas of modern data science.

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 before:
The application period is closed. Check again in September.
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
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"
SLT
Track "Statistical Learning Theory"
* only in network form with HSE


MLAI
DIMMS
SLT

Coursework (36 credits)
Statistical Natural Language Processing
Omics Technologies
Geometric Computer Vision
Soft Matter in Practice
Modern Methods of Data Analysis: Stochastic analysis (HSE course)
Methods of Multidimensional Statistics (HSE course)
Modern Algorithmic Optimization (HSE course)
Probabilistic Graphic Models (HSE course)
Elective from the HSE Catalog "MAGO-LEGO" (HSE course)

Elective courses (24 credits)

Entrepreneurship and Innovation (12 credits)
Technological Innovations: from Research Results to Commercial Product

Research and MSс Thesis Project (36 credits)

Industry Immersion (12 credits)
Academic Mobility Partners:
  • Aalborg University
  • Bell Laboratories
  • Columbia University
  • Dartmouth College
  • DLR German Aerospace Center
  • École Polytechnique Fédérale de Lausanne (EPFL)
  • ETH Zurich
  • Facebook
  • Far East Federal University (FEFU)
  • Fondazione Bruno Kessler
  • Jilin University
  • King Abdullah University of Science and Technology
  • Massachusetts Institute of Technology (MIT)
  • New York University (NYU)
  • Ohio State University
  • Philips Russia
  • JSC SOYUZNAB
  • Technical University of Munich
  • University of Edinburgh
  • University of Helsinki
  • University of Kaiserslautern
  • University of North Carolina
  • Wigner Research Center for Physics
Industry Immersion Partners:
  • Boeing
  • BOSCH
  • EY
  • GazpromNeft
  • Yandex
  • Google
  • Huawei
  • Nissan
  • Nokia
  • nVidia
  • Ozon
  • PwC
  • Russian Quantum Center
  • Samsung
  • SAP
  • Sberbank
  • Sibur
  • Tinkoff Bank
  • MTS
  • and more
Main research areas:
  • Machine Learning, Deep Learning and Artificial Intelligence
  • Industrial Data Analytics
  • Computer Vision, Robotics & Visual Analytics
  • Biomedical Analytics and Systems
  • Natural Language Processing
  • Digital Agronomics & Pharmaceutical Design
  • Chemo- and Bioinformatics
  • Efficient Numerical Algorithms
  • Computational Soft Matter and Complex Systems
  • High-dimensional Statistics and Statistical learning
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 (online)
The final admission stage will take place online. You will need to pass a face-to-face interview, a soft skills challenge and an online Math exam.
Exam samples:
Keep in mind that the structure of exam can be changed
following the decision of program committee.
I cannot help but smile as I remember my crazily productive period during Skoltech's Master's program. Adapting to a drastic change of atmosphere (moving from Peru and a different academic background) was certainly a tough challenge. However, the impact this program had in my career, the amazing friendships acquired and the exposure to numerous opportunities, made it worth it. Overall, the whole coursework of the Data Science program provided me confidence and a wide range of skills to tackle Machine Learning projects both from an industrial and research perspective. Undoubtedly, one of the best choices of my life.
Alfredo De La Fuente
Universidad Nacional de Ingeniería → Skoltech
Studying at Skoltech is the ultimate first step towards new achievements. Not a single master's program in Russia provides you with so many opportunities in one place: training in English, fascinating lectures, world-class professors, joint projects with industrial companies, support for personal startups. Skoltech is filled with people who are passionate about their work and enthusiastically share their experience with students. It inspires and helps you move forward.
Tatiana Medvedeva
Lomonosov Moscow State University → Skoltech
Faculty
The program has a globally renowned faculty with international experience
and a broad network of collaborations:
Application
The application period for Skoltech Master's programs for the 2020-2021 academic year is now closed. To apply for the 2021-2022 academic year, check back in autumn or subscribe below to receive a notification.
Contact
e-mail: admissions@skoltech.ru
phone: +7 (495) 280-1481_ext.3387
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