Master’s programme in Big Data & Data Science

Big data refers to the massive amounts of information becoming available as people use the internet and computers more

This data can generate powerful insights in domains ranging from business and entertainment to politics and healthcare

However, the amount of data being captured today is ever-increasing and extends beyond the storage and analyzing capacity of traditional applications

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Graduates of the programme will have a unique combination of skills in data science and data management


These skills allow them to comprehend, process and manage data effectively and efficiently, to extract value from data, to visualise it and to communicate it


The programme serves increasing demands from industry which relies upon massive volumes of data


Russian and other companies such as IBM, Infosys, Oracle and Orion increasingly need specialists in this field

Programme options

As a Master student, you'll choose one of the following specialisations.

  • Basics of biotechnology and biochemistry
  • Introduction to bioinformatics
  • Cell biology and molecular biology
  • Molecular sequence analysis
  • Molecular genetics and comparative genomics

Data Science
  • Information security and working with personal data
  • Methodology and methods of research and analysis of social media data
  • Analysis and modelling social and political processes
  • Psychodiagnosis and psychological analysis of social systems

Ведущие преподаватели

Dr Daniel Stamate

Department of Computing, Goldsmiths College – University of London
My present research focuses on Machine Learning and Statistical Learning, with particular interests in:

  • Sentiment analysis & stock market forecasting;
  • Prediction modelling and computational psychiatry;
  • Machine and statistical learning modelling to understand heterogeneous manifestations of asthma in early life;
  • Predicting risk of dementia using routine primary care records, forthcoming work;
  • Mobility big data analytics – in particular focusing on analysing smart card (Oyster) data of Transport for London.

Boris Shilov

Assistant professor,
Pirogov Russian National Research Medical University · bioinformaticsAreas of expertise:
computational approach to biomedical imaging; bioinformatics and systems biology.
Dr. Shilov is one of the few professionals in bioinformatics in Tomsk.
Course within the Program “Introduction to bioinformatics”.

Thomas B. Preußer

Council member, Lecturer at the Department
of Computer Science at TU Dresden
Research Interests:

  • Ecological modelling and statistical data analysis
  • Phytoplankton, cyanobakteria, zooplankton, makrozoobenthos
  • Eutrophication, sediment-water-interaction, antibiotica resistance
  • Lakes, reservoirs, streams, laboratory systems
  • Systems understanding and development of modelling tools

Igor Sharakhov

Associate Professor Department of Entomology
Virginia Polytechnic Institute
«As a member of the Vector-Borne Disease Group at Virginia Tech, I am broadly interested in genomics and evolutionary cytogenetics of mosquitoes – vectors of human infectious diseases. My goal is to understand the genetic mechanisms of mosquito evolution, adaptation, and reproduction. This knowledge can facilitate the development of innovative genome-based approaches for mosquito vector control».
Invited to the University for the course “Comparative genomics”.

Овсянников Михаил Сергеевич

Ассистент кафедры теоретических основ информатики ТГУ
С отличием закончил факультет информатики в 2008 году. На данный момент Михаил находится в процессе подготовки к защите кандидатской диссертации. Научные интересы: Высокопроизводительные распределенные и облачные системы.
«Лучшие научные результаты всегда достижимы на стыке наук. Множество различных областей знания способны совершить рывок в развитии, если научиться эффективно обрабатывать огромные массивы данных, получаемые как результаты расчетов или измерений».

Консультанты программы и партнеры


Вирджинский технический университет (США)


Российский национальный исследовательский медицинский университет им. Пирогова (Москва)


Сибирский государственный медицинский университет (Томск)


Arizona State University (США)


Goldsmiths Соllege, Университет Лондона (Великобритания)


Дрезденский технический университет (Германия)


Сколковский институт наук и технологий (Москва)


Рейтинговое агентство «Эксперт» (Москва)

Facts and figures

Title/degree: Master of Science (MSc)
Duration: 2 years (120 EC), full-time
Start month: September
Language of instruction: Russian/English
Programme code: 01.04.02 Applied Mathematics and Informatics
Department: Institute “Human of the digital era”

Core courses

  • Intro to Data Mining
  • Practical Machine Learning
  • High Performance Data Processing (Map Reduce, Hive, MPI, NoSQL and others)
  • Cloud Computing
  • Script Languages (Python, R)
  • Postrelational databases
  • Decision Support and Data analysis
To participate in the interview, please fill out a registration form on the program website:


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To know more about the programme Big Data & Data Science, please contact:
Olga Marukhina (program manager)
Academic office: TSU Building № 2, office 038