DATA
SCIENCE
Master of Science
in Mathematics and Computer Science
Data scientists are already among the most demanded specialists in the hi-tech market. The goal of this program is 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:
Applications for the next selection wave are accepted until July 16
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 employment.
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 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 the 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 the solution of real-world problems
Track
Math of Machine Learning (MML)
(in network form with Higher School of Economics)
Modern Machine Learning is at the cutting edge of various disciplines of mathematics and computer science. Math of Machine Learning 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 Skoltech and Higher School of Economics 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
    MML
    Track "Math of Machine Learning"*
    *only in network form with HSE University

    MLAI
    MML
    Compulsory and Recommended elective courses (36 credits)

    Compulsory courses:
    Numerical Linear Algebra
    Machine Learning
    Deep Learning

    Recommended electives:
    Introduction to Data Science
    Introduction to Artificial Intelligence
    Computational Imaging
    Foundations of Software Engineering
    Efficient Algorithms and Data Structures
    Theoretical Methods of Deep Learning
    Convex Optimization and Applications
    Introduction to Computer Vision
    Statistical Natural Language Processing
    Principles of Applied Statistics
    Perception in Robotics
    Introduction to Digital Agro
    Advanced Statistical Methods
    Statistical Learning Theory
    Geometric Computer Vision
    Biomedical Imaging and Analytics
    Geometrical Methods of Machine Learning
    Neural Natural Language Processing
    Planning Algorithms in Artificial Intelligence
    Bayesian Methods of Machine Learning
    Matrix and Tensor Factorizations
    Neuroimaging and Machine Learning for Biomedicine
    Reinforcement Learning
    Models of Sequential Data
    Stochastic Calculus (HSE University)
    Random Matrix Theory (HSE University)
    Neurobayesian Models (HSE University)
    Research seminar (HSE University)

    Elective courses (24 credits)

    Entrepreneurship and Innovation (12 credits)
    Technology Entrepreneurship: Foundation
    Entrepreneurial Strategy
    Leadership for Innovators
    Hack Lab: Laboratory for Ideas
    Startup Workshop
    Ideas to Impact: Foundations for Commercializing Technological Advances
    Biomedical Innovation and Entrepreneurship
    IoT: Launching New Products & Startups
    Business Communication
    Technology Planning and Roadmapping: Foundation
    Technology Planning and Roadmapping: Advanced
    Intellectual Property, Technological Innovation and Entrepreneurship
    Technology Entrepreneurship: Advanced
    Technological Innovations: from Research Results to a Commercial Product
    Developing Products and Services through Design Thinking

    Research and MSс Thesis Project (36 credits)

    Industry Immersion (12 credits)
    The program is in a network form with:
    • HSE University
    • MIPT
    Industrial Partners:
    • Sberbank
    • Yandex
    • RusAgro
    • VisionLabs
    • Datadvance
    • ScanEx
    • Geoscan
    • Gazpromneft
    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
    Main research areas:
    • Industrial Analytics
    • Machine Learning
    • Deep Learning
    • Computational Imaging & Graphics
    • Computer Vision
    • Robotics
    • Computational Intelligence
    • Digital Agriculture
    • FinTech
    • Natural Language Processing
    • Reinforcement Learning
    Research groups:

      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:
      Bachelor's degree or its equivalent in Mathematics, Computer Science, Information and Communication Technology, 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
      Prepare your portfolio
      Prepare your application materials.
      2
      Submit your application
      Upload your materials into the application system by July 16, 2021 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, an entrepreneurship 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.
      what our students say
      True Stories of our students
      Faculty
      The program has a globally renowned faculty with international experience and a broad network of collaborations:
      Instructors at HSE University
      Apply now
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      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