About this Course

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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.

Approx. 20 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.

Approx. 20 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

3 hours to complete

Basic Combinatorics

3 hours to complete
13 videos (Total 44 min), 3 readings, 12 quizzes
13 videos
Rule of Sum3m
Convenient Language: Sets2m
Generalizing Rule of Sum1m
Recursive Counting: Number of Paths3m
Rule of Product5m
Number of Tuples2m
Set Language for Tuples2m
Licence Plates2m
Tuples with Restrictions2m
Permutations4m
Unordered Pairs3m
Combinations7m
3 readings
Slides10m
Slides10m
Slides10m
12 practice exercises
Rule of Sum in Programming5m
Numbers Divisible by 2 or 38m
Sets and Operations with Them12m
Generalized Rule of Sum10m
Number of Paths10m
Rule of Product8m
Rule of Product in Programming12m
Tuples8m
Tuples with Restrictions12m
Number of Segments15m
Nested For Loops5m
Splitting Datasets15m
Week
2

Week 2

4 hours to complete

Advanced Combinatorics

4 hours to complete
11 videos (Total 46 min), 6 readings, 12 quizzes
11 videos
Binomial Theorem4m
Practice Counting6m
Review3m
Salad3m
Combinations with Repetitions4m
Distributing Assignments Among People2m
Distributing Candies Among Kids2m
Numbers with Fixed Sum of Digits4m
Numbers with Non-increasing Digits1m
Splitting into Working Groups3m
6 readings
Slides10m
Generation of Combinatorial Objects10m
Number of Salads10m
Producing the list of salads10m
Slides10m
Slides10m
12 practice exercises
Comparing Binomial Coefficients10m
Sums of Binomial Coefficients15m
Applying Binomial Theorem7m
Practice Counting10m
Number of Salads10m
Combinations with Repetitions10m
Distributing Assignments Among People10m
Distributing Candies Among Kids10m
Numbers with Fixed Sum of Digits10m
Numbers with Non-increasing Digits10m
Splitting into Working Groups10m
Problems in Combinatorics40m
Week
3

Week 3

3 hours to complete

Discrete Probability

3 hours to complete
7 videos (Total 75 min)
7 videos
Operations on events9m
Classical probability9m
Probabilities and combinatorics10m
Probabilities and operations on events10m
Analysis of bagging procedure10m
Outcomes with non-equal probabilities12m
6 practice exercises
Experiments, outcomes and events15m
Operations on events20m
Probabilities by definition20m
Probabilities and combinatorics10m
Finding probabilities using rules10m
Outcomes with non-equal probabilities15m
Week
4

Week 4

2 hours to complete

Introduction to Graphs

2 hours to complete
9 videos (Total 36 min), 3 readings, 6 quizzes
9 videos
Trees4m
Colorings. Bipartite Graphs3m
Konigsberg Bridges. Euler Cycles4m
Constructing a Euler Cycle4m
Hamiltonian Paths5m
Acyclic Directed Graphs. Topological Sorting3m
Traversing Trees3m
Traversing Graphs: DFS and BFS3m
3 readings
Slides10m
Slides10m
Slides10m
6 practice exercises
Trees10m
Coloring15m
Euler Path10m
Hamiltonian Cycle5m
Topological Sorting5m
Traversing trees5m

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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