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Mathematics for Data Science

Build a Foundation for your Data Science Skills.

Start date will be announced later
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  • Additional education for children and adults

    level of education

  • English

    program language

  • 22 weeks

    program duration

  • about 4 hours per week

    needed to educate

  • 10 credit points

    360 academic hour

  • Cost 17 000 Р

    for studying

Master a wide range of math underlying Data Science and learn how to apply it in practice.

Learning courses of specialization take place on the platform online.hse.ru. Preview of the materials and payment for the specialization at the link.

About program

Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. We will cover such crucial fields as Discrete Mathematics, Calculus, Linear Algebra and Probability. To make your experience more practical we accompany mathematics with examples and problems arising in Data Science and show how to solve them in Python.

Format

The course takes place on the internal platform of the Higher School of Economics. 

Each week contains video lectures, tests and materials for independent study. Each course of the specialisation ends with a project that gives an opportunity to see how the material of the course is used in Data Science. Each project is directed at solving practical problem in Data Science. In particular, in your projects you will analyse social graphs, predict estate prices and uncover hidden relations in the data.

This Specialization is part of HSE University Master of Data Science degree program.

Requirements

  • - basic math basic knowledge
  • - discrete mathematics (combinatorics) and calculus (derivatives, integrals)
  • - basic programming in Python

    • Study plan

    1. Discrete Math and Analyzing Social Graphs
    2. Calculus and Optimization for Machine Learning
    3. First Steps in Linear Algebra for Machine Learning
    4. Probability Theory, Statistics and Exploratory Data Analysis
    Национальный исследовательский университет «Высшая школа экономики»

    Чернышев Всеволод Леонидович

    Кандидат физико-математических наук, Доцент
    Position: Ведущий научный сотрудник: Факультет компьютерных наук

    Пионтковский Дмитрий Игоревич

    Доктор физико-математических наук
    Position: Старший научный сотрудник, профессор: Факультет экономических наук

    Бардуков Анатолий Андреевич

    Аспирант
    Position: Преподаватель: Факультет компьютерных наук / Департамент больших данных и информационного поиска

    Зимонов Андрей Вадимович


    Position: Старший преподаватель: Факультет компьютерных наук

    Космачев Алексей Дмитриевич


    Position: Преподаватель: Факультет Математических Наук

    Кузнецов Степан Львович

    Кандидат физико-математических наук, Доцент
    Position: Старший научный сотрудник: Факультет компьютерных наук

    Подольский Владимир Владимирович

    Доктор физико-математических наук
    Position: Ведущий научный сотрудник Международной лаборатории теоретической информатики

    Савостьянов Антон Сергеевич


    Position: Преподаватель: Факультет Компьютерных Наук

    Щуров Илья Валерьевич

    Кандидат физико-математических наук
    Position: Доцент кафедры высшей математики

    Courses in program

    17 October 2023 - 1 January 2033 г.
    The course has already started
    Discrete Math and Analyzing Social Graphs
    НИУ ВШЭ
    17 October 2023 - 1 January 2033 г.
    The course has already started
    Calculus and Optimization for Machine Learning
    НИУ ВШЭ
    17 October 2023 - 1 January 2033 г.
    The course has already started
    First Steps in Linear Algebra for Machine Learning
    НИУ ВШЭ
    17 October 2023 - 1 January 2033 г.
    The course has already started
    Probability Theory, Statistics and Exploratory Data Analysis
    НИУ ВШЭ