About this Course

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Flexible deadlines

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Intermediate Level

Some background in Python programming language and algebra.

Approx. 14 hours to complete

English

Subtitles: English

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Some background in Python programming language and algebra.

Approx. 14 hours to complete

English

Subtitles: English

Offered by

National Research University Higher School of Economics logo

National Research University Higher School of Economics

Start working towards your Master's degree

This course is part of the 100% online Master of Data Science from National Research University Higher School of Economics. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

Week
1

Week 1

5 hours to complete

Systems of linear equations and linear classifier

5 hours to complete
15 videos (Total 118 min), 2 readings, 2 quizzes
15 videos
Introduction to Linear Algebra42s
Linear Algebra and Calculus4m
Matrices and Multidimensional Vectors10m
Matrix arithmetics6m
Properties of matrix operations and some special matrices10m
Vectors and matrices in Python4m
Systems of linear equations11m
Matrix inverse13m
Gaussian elimination. The first example4m
Elementary row operations6m
Gaussian elimination. Main theorem.5m
Gaussian Elimination. The algorithm.13m
The Inverse matrix with Gaussian elimination5m
LU and PLU decomposition17m
2 readings
About the University10m
Covered Python methods20m
1 practice exercise
Week 11h
Week
2

Week 2

2 hours to complete

Full rank decomposition and systems of linear equations

2 hours to complete
14 videos (Total 86 min)
14 videos
Abstract algebra and linear algebra11m
Axioms of vector spaces: first application6m
Examples of vector spaces8m
Subspaces1m
Linear combinations and spans2m
Basis and linear dependence7m
Dimension of a vector space5m
Examples of bases7m
Linear dependence and rank3m
Formula for the solution of a SLAE9m
An example of vector representation of the set of solutions7m
Rouché–Capelli Theorem4m
Full rank decomposition8m
1 practice exercise
Week 230m
Week
3

Week 3

2 hours to complete

Euclidean spaces

2 hours to complete
10 videos (Total 85 min)
10 videos
Coordinates change example9m
Euclidean space8m
Geometry and Euclidean spaces1m
Orthogonal and orthonormal bases4m
Distance and orthogonal projections6m
Inconsistent systems and the least squares method12m
Linear regression example8m
Introduction to support vector machine16m
Linear regression and SVM with Python4m
1 practice exercise
Week 330m
Week
4

Week 4

4 hours to complete

Final Project

4 hours to complete
1 video (Total 2 min), 1 reading, 2 quizzes
1 reading
References and further reading10m
1 practice exercise
Life expectancy prediction quiz1h

About the Mathematics for Data Science Specialization

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....
Mathematics for Data Science

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

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