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.
Coursework & Graduation Requirements
Based on the track you select – Data Scientist, Machine Learning Engineer, Researcher in Data Science – you’ll complete a different set of courses that count towards your degree. Each 5-credit course is about six weeks long.
|Data Scientist Track||Machine Learning Engineer Track||Researcher in Data Science Track|
|Semester 1, Course 1||Python - Basic & Advanced, Algorithms and Data Structures Parts I & II||Python - Basic & Advanced, Algorithms and Data Structures Parts I & II||Python - Basic & Advanced, Algorithms and Data Structures Parts I & II|
|Semester 1, Course 2||Discrete Mathematics, Calculus, Linear Algebra, Probability Theory||Discrete Mathematics, Calculus, Linear Algebra, Probability Theory||Discrete Mathematics, Calculus, Linear Algebra, Probability Theory|
|Semester 2, Course 1||Statistics Basic, SQL, Machine Learning, Applied Machine Learning||Statistics Basic, SQL, Machine Learning, Applied Machine Learning||Statistics Basic, SQL, Machine Learning, Applied Machine Learning|
|Semester 2, Course 2||Data Scraping Project, Applied Statistics, Introduction to Deep Learning||Data Scraping Project, Applied Statistics, Introduction to Deep Learning||Data Scraping Project, Computational Complexity, Computational Learning Theory|
|Semester 3, Course 1||Machine Learning Project, Large Scale Machine Learning 1 & 2||Machine Learning Project, C++||Machine Learning Project, Optimization for Machine Learning, Advanced Algorithms|
|Semester 3, Course 2||Machine Learning Project, Computer Vision, NLP||Machine Learning Project, DevOps, OOP and Software Architecture||Machine Learning Project, Bayesian Methods for ML, Deep Generative Methods|
|Final Project||All tracks||All tracks||All tracks|
|Final Project Presentation||All tracks||All tracks||All tracks|
NEW! Specialization & Open Courses
The Specialization, Mathematics for Data Science, is now open for enrollment on Coursera. There are four courses in this Specialization:
- Discrete Math and Analyzing Social Graphs
- Calculus and Optimization for Machine Learning
- First Steps in Linear Algebra for Machine Learning
- Probability Theory, Statistics and Exploratory Data Analysis
If you are admitted to the full program, any progress you make in these courses will count towards your degree learning.
The program is designed so students can enroll from anywhere in the world and complete courses at their own pace. As a designated two-year program, it is recommended that students take between 20-to-24 months to complete the program.
The program is 100% online and operates with a pay-by-semester model. The online format of the MDS also allows students to interact with instructors and teaching assistants regularly through live chats and video conferences.
Coursera on Mobile
Access all course materials anywhere with the mobile app, used by over 80 percent of degree students on Coursera. Available on iOS and Android.
Using the mobile app, learners can:
- Save a week’s worth of content for offline access with one click
- Save and submit quizzes offline
- View text transcripts of lecture videos
- Take notes directly in the app
- Set reminder alerts to help you make progress
Download Coursera's mobile app
Coursera does not grant credit, and does not represent that any institution other than the degree granting institution will recognize the credit or credential awarded by the institution; the decision to grant, accept, or transfer credit is subject to the sole and absolute discretion of an educational institution.
We encourage you to investigate whether this degree meets your academic and/or professional needs before applying.