Master of Data Science

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Overview

The first fully online Master of Data Science from a top-10 Russian university, featuring applied projects with industry partners like Yandex.

Recently ranked first place in Forbes' "Universities for the Future Elite: 100 Best Russian Universities According to Forbes – 2020" ranking, National Research University Higher School of Economics (HSE University) is a leading university for multidisciplinary research, international cooperation, and digital education in Russia. A dynamic institution serving more than 40,000 students at campuses in Moscow, Saint-Petersburg, Nizhny Novgorod, and Perm, HSE University is a leader in combining Russia’s education traditions with the best international practices in teaching and research.

HSE’s Master of Data Science is the first fully English-taught online data science Master’s from a Russian university. Unlike many online degrees in Russia and the Commonwealth of Independent States, this degree has no on-campus requirements. All meetings, coursework, and live class sessions are taught in English and replicate the practitioner’s work environment of real data scientists using Python, Github, and modern apps like Slack and Zoom.

What makes this data science Master's degree unique? Collaborate with some of Russia’s best technology companies, like Yandex

HSE has partnerships with some of Russia’s largest and most impressive technology companies, such as Yandex. HSE’s Yandex partnership is a deep collaboration, with several faculty members working concurrently at Yandex and HSE. In this degree, you will complete applied projects designed with HSE in collaboration with its industrial partners.

A degree with multiple tracks for students of all backgrounds

This degree is designed for students with or without prior coding experience. HSE provides a career-relevant curriculum for analysts, data and computer scientists, software engineers, academic researchers, or machine learning engineers by utilizing three distinct tracks: Data Scientist, Machine Learning Engineer, and Researcher in Data Science.

Applied projects with expert faculty guidance

Throughout the degree, students are paired with faculty member advisors and industry experts as they work through their final project, which involves solving a real-world problem with techniques taught in the program. Students benefit by making connections with faculty members and learning from some of the most impressive technology companies in Russia and beyond. In addition, they are encouraged to connect with faculty during live class sessions and weekly office hours with tools like Slack and Zoom.

Syllabus

The program curriculum consists of 21 courses divided into four blocks: a mathematics block, a programming block, a professional block, and a project block. Students begin their first semester with courses in the mathematics and programming blocks. Each course is typically 5 credits, while a project is 10 credits. In order to successfully complete the program, students must earn 120 ECTS (European Credit Transfer System) credits in total. 10 credits will come from the final project presentation.

Every course includes at least one live session with a faculty member through tools like Slack and Zoom, weekly office hours, one assignment graded personally by a faculty member, and one hands-on project.

To graduate, each student will complete a final project, which will involve solving a problem with a real-world dataset. For this project, students are assigned their own advisor that provides feedback and guidance. In the last two weeks of the program, students get the opportunity to present their project to a committee of academic supervisors and industry professionals via Zoom. The presentation will be approximately 10-15 minutes, followed by Q&A and scoring by the panel. All coursework must be completed prior to the final presentation.

Upon successful completion of the degree program, students will receive a Master of Data Science degree from HSE University.

Featuring participation from industry partner, Yandex

Established in 1997, Yandex is one of Russia's leading technology companies in search, on-demand transportation services, and other mobile applications used by millions of consumers around the world. Yandex is an industry partner of HSE's online Master of Data Science program. Through this partnership, Yandex lends its expertise in data science by developing some of the program's courses. Representatives from Yandex will also participate in the final project presentations, as well as conduct mock interviews for the program’s top-achieving students.

Courses

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4 weeks
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The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. 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.

This online course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well.

This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.
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6 weeks
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Hi! Our online course aims to provide necessary background in Calculus sufficient for up-following Data Science courses.

Course starts with a basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus sequentially building up a base for the basic optimisation. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning.

Additional materials provided during the course include interactive plots in GeoGebra environment used during lectures, bonus reading materials with more general methods and more complicated basis for discussed themes.

This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.
1 review
6 weeks
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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. We will illustrate new knowledge, for example, by counting the number of features in data or by estimating the time required for a Python program to run.

Next, we will apply our knowledge in combinatorics to study basic Probability Theory. Probability is everywhere in Data Analysis and we will study it in much more details later. Our goals for probability section in this course will be to give initial flavor of this field.

Finally, we will study the combinatorial structure that is the most relevant for Data Analysis, namely graphs. Graphs can be found everywhere around us and we will provide you with numerous examples. We will mainly concentrate in this course on the graphs of social networks. We will provide you with relevant notions from the graph theory, illustrate them on the graphs of social networks and will study their basic properties. In the end of the course we will have a project related to social network graphs.

As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in Python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in Data Analysis, starting from motivated high school students.

This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.
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6 weeks
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Exploration of Data Science requires certain background in probability and statistics. This online course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science.

The core concept of the course is random variable — i.e. variable whose values are determined by random experiment. Random variables are used as a model for data generation processes we want to study. Properties of the data are deeply linked to the corresponding properties of random variables, such as expected value, variance and correlations. Dependencies between random variables are crucial factor that allows us to predict unknown quantities based on known values, which forms the basis of supervised machine learning. We begin with the notion of independent events and conditional probability, then introduce two main classes of random variables: discrete and continuous and study their properties. Finally, we learn different types of data and their connection with random variables.

While introducing you to the theory, we'll pay special attention to practical aspects for working with probabilities, sampling, data analysis, and data visualization in Python.

This course requires basic knowledge in Discrete mathematics (combinatorics) and calculus (derivatives, integrals).

This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.

Master of Data Science

Overview

Syllabus

Courses

First Steps in Linear Algebra for Machine Learning

Calculus and Optimization for Machine Learning

Discrete Math and Analyzing Social Graphs

Probability Theory, Statistics and Exploratory Data Analysis

Tags

First Steps in Linear Algebra for Machine Learning

Calculus and Optimization for Machine Learning

Discrete Math and Analyzing Social Graphs

Probability Theory, Statistics and Exploratory Data Analysis

Tags

Master of Applied Data Science

Master of Machine Learning and Data Science

Statistics and Data Science

Master of Computer Science in Data Science

Online Master of Computer Science

Master of Science in Data Science

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