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Mathematics for Data Science

Vladimir Podolskii, Ilya V. Schurov, Anton Savostianov, Dmitri Piontkovski, Vsevolod L. Chernyshev, Stepan Kuznetsov, and Владимир Подольский

This Specialization is part of HSE University Master of Data Science degree program. Learn more about the admission into the program here and how your Coursera work can be leveraged if accepted into the 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.

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What's inside

Four courses

First Steps in Linear Algebra for Machine Learning

(0 hours)

The main goal of this course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability.

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Discrete Math and Analyzing Social Graphs

(0 hours)

The main goal of this online course is to introduce topics in Discrete Mathematics relevant to Data Analysis. We will start with a brief introduction to combinatorics, the branch of mathematics that studies how to count. Basics of this topic are critical for anyone working in Data Analysis or Computer Science.

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Calculus and Optimization for Machine Learning

(0 hours)

Hi! Our online course provides the Calculus background needed for Data Science courses. We start with functional mappings and progress to limits, differentiability, integration, and optimization. The course includes a programming project using optimization in machine learning. Interactive plots and bonus reading materials are provided.

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Probability Theory, Statistics and Exploratory Data Analysis

(1 hours)

Exploration of Data Science requires a background in probability and statistics. This online course introduces you to the necessary sections of probability theory and statistics, guiding you from the basics to the level required for Data Science.

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Time

2 hours

Via

Coursera

Institutions

National Research University Higher School of Economics

HSE University

Instructors

Vladimir Podolskii

Ilya V. Schurov

Anton Savostianov

Dmitri Piontkovski

Vsevolod L. Chernyshev

Stepan Kuznetsov

Владимир Подольский

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