Data Science
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Master
In Moscow (Russia)
Open new job opporunities with this master's degree
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Type
Master
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Location
Moscow (Russia)
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Duration
2 Years
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Start date
Different dates available
Emagister presents to you the Master in Data Science endorsed by the Skolkovo Institute of Science and Technology.
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.
If you need more information, do not hesitate to contact the centre through Emagister.
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About this course
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.
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
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.
Calculus, Differential Equations, Linear algebra, Probability theory and mathematical statistics, Discrete mathematics (including graph theory and basic algorithms), Programming.
IT related bachelor's degree, or its equivalent in Mathematics, Computer Science, Information and Communication Technology, Applied Physics or other technical areas.
If your education has not been conducted in English, you will be expected to demonstrate evidence of an adequate level of English proficiency.
Reviews
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Skolkovo Institute of Science and Technology is one of the best institute.
← | →
-
Interesting
← | →
-
Extreme
← | →
Course rating
Recommended
Centre rating
Umar Khan
Bamidele Bamidele
Rushikesh Bagde
Rushikesh Bagde
Gabriel Lara
Student
Mark Vinogradov
Andrei Davydov
Brandan Willcocks
Subjects
- Technology
- Innovation
- Entrepreneurship
- Data analysis3
3 students say they acquired this skill
- Data Protection
22 students say they acquired this skill
- Mathematics
33 students say they acquired this skill
- Algebra
11 students say they acquired this skill
- Maths
- Digital Editing
- Data Management
11 students say they acquired this skill
- Image processing
- Modelling
- Applications
11 students say they acquired this skill
- Machine Learning
22 students say they acquired this skill
- Analytics
22 students say they acquired this skill
- Process
11 students say they acquired this skill
- Data science
44 students say they acquired this skill
- Robotics
11 students say they acquired this skill
- Blockchain
- Machine Learining
11 students say they acquired this skill
- Coding Theory
- Biomedical Imaging
11 students say they acquired this skill
Teachers and trainers (1)
Faculty members Members
teachers and administrators
Course programme
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.
Coursework (36 credits)
- Introduction to Data Science
- Efficient Algorithms and Data Structures
- Numerical Linear Algebra
- Machine Learning
- Theoretical Methods of Deep Learning
- Information and Coding Theory
- Large-scale Optimization and Applications
- Deep Learning
- Bayesian Methods of Machine Learning
- Matrix and Tensor Factorizations
- Introduction to Artificial Intelligence
- Computational Imaging
- Digital Signal Processing
- Introduction to Computer Vision
- Introduction to Blockchain
- Convex Optimization and Applications
- Advanced Statistical Methods
- Perception in Robotics
- Biomedical Imaging and Analytics
- Statistical Natural Language Processing
- Geometrical Methods of Machine Learning
- Introduction to Digital Agro
- Omics Technologies
- Uncertainty Quantification
- Neural Natural Language Processing
- Geometric Computer Vision
- Foundations of Multiscale Modelling: Kinetics
- Scientific Computing
- Thermodynamics and Transport at Nanoscale
- Numerical Modeling
- High Performance Computing and Modern Architectures
- Stochastic Methods in Mathematical Modelling
- Machine Learning in Chemoinformatics
- 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)
Entrepreneurship and Innovation (12 credits)
- Innovation Workshop
- Ideas to Impact: Foundations for Commercializing Technological Advances
- Leadership for Innovators
- Business Communication
- Biomedical Innovation and Entrepreneurship
- Intellectual Property and Technological Innovation
- 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
Industry Immersion (12 credits)
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 problemscontribute 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 fieldsapply relevant software tools, algorithms, data models, and computational environments for the solution of real-world problems
Track: Math for 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 optimizationbe able to apply and further develop such methods for solving complex practically motivated problems of data analysisAdditional information
Data Science
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