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Data Science Math Skills

Duke University

About this Course

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Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. 
Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.

Topics include:
~Set theory, including Venn diagrams
~Properties of the real number line
~Interval notation and algebra with inequalities
~Uses for summation and Sigma notation
~Math on the Cartesian (x,y) plane, slope and distance formulas
~Graphing and describing functions and their inverses on the x-y plane,
~The concept of instantaneous rate of change and tangent lines to a curve
~Exponents, logarithms, and the natural log function.
~Probability theory, including Bayes’ theorem.

While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization.  Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel." 

Good luck and we hope you enjoy the course!

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Beginner Level

Approx. 13 hours to complete

English

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Skills you will gain

Bayes' Theorem
Bayesian Probability
Probability
Probability Theory

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Approx. 13 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.46/5 (3,077 Ratings)

Daniel Egger

Executive in Residence and Director, Center for Quantitative Modeling

Pratt School of Engineering, Duke University

1,001,157 Learners

8 Courses

Paul Bendich

Assistant research professor of Mathematics; Associate Director for Curricular Engagement at the Information Initiative at Duke

Mathematics

348,618 Learners

2 Courses

Offered by

Duke University

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Syllabus - What you will learn from this course

Content Rating96%(45,169 ratings)

Week

Week 1

18 minutes to complete

Welcome to Data Science Math Skills

18 minutes to complete

1 video (Total 3 min), 2 readings

4 hours to complete

Building Blocks for Problem Solving

This module contains three lessons that are build to basic math vocabulary. The first lesson, "Sets and What They’re Good For," walks you through the basic notions of set theory, including unions, intersections, and cardinality. It also gives a real-world application to medical testing. The second lesson, "The Infinite World of Real Numbers," explains notation we use to discuss intervals on the real number line. The module concludes with the third lesson, "That Jagged S Symbol," where you will learn how to compactly express a long series of additions and use this skill to define statistical quantities like mean and variance.

4 hours to complete

10 videos (Total 93 min), 4 readings, 4 quizzes

10 videos

Sets - Basics and Vocabulary9m

Sets - Medical Testing Example11m

Sets - Venn Diagrams7m

Numbers - The Real Number Line9m

Numbers - Less-than and Greater-than6m

Numbers - Algebra With Inequalities10m

Numbers - Intervals and Interval Notation7m

Sigma Notation - Introduction to Summation9m

Sigma Notation - Simplification Rules7m

Sigma Notation - Mean and Variance12m

4 readings

A note about the video lectures in this lesson3m

A note about the video lectures in this lesson10m

Feedback10m

4 practice exercises

Practice quiz on Sets15m

Practice quiz on the Number Line, including Inequalities25m

Practice quiz on Simplification Rules and Sigma Notation20m

Graded quiz on Sets, Number Line, Inequalities, Simplification, and Sigma Notation35m

Week

Week 2

3 hours to complete

Functions and Graphs

3 hours to complete

8 videos (Total 72 min), 3 readings, 3 quizzes

8 videos

Cartesian Plane - Plotting Points7m

Cartesian Plane - Distance Formula10m

Cartesian Plane - Point-Slope Formula for Lines8m

Cartesian Plane: Slope-Intercept Formula for Lines7m

Functions - Mapping from Sets to Sets7m

Functions - Graphing in the Cartesian Plane11m

Functions - Increasing and Decreasing Functions10m

Functions - Composition and Inverse10m

3 readings

A note about the video lectures in this lesson3m

Feedback10m

3 practice exercises

Practice quiz on the Cartesian Plane15m

Practice quiz on Types of Functions20m

Graded quiz on Cartesian Plane and Types of Function40m

Week

Week 3

3 hours to complete

Measuring Rates of Change

This module begins a very gentle introduction to the calculus concept of the derivative. The first lesson, "This is About the Derivative Stuff," will give basic definitions, work a few examples, and show you how to apply these concepts to the real-world problem of optimization. We then turn to exponents and logarithms, and explain the rules and notation for these math tools. Finally we learn about the rate of change of continuous growth, and the special constant known as “e” that captures this concept in a single number—near 2.718.

3 hours to complete

7 videos (Total 66 min), 3 readings, 3 quizzes

7 videos

Tangent Lines - Slope of a Graph at a Point10m

Tangent Lines - The Derivative Function9m

Using Integer Exponents7m

Simplification Rules for Algebra using Exponents11m

How Logarithms and Exponents are Related12m

The Change of Base Formula4m

The Rate of Growth of Continuous Processes11m

3 readings

A note about the video lectures in this lesson10m

A note about the video lectures in this lesson3m

Feedback10m

3 practice exercises

Practice quiz onTangent Lines to Functions10m

Practice quiz on Exponents and Logarithms40m

Graded quiz on Tangent Lines to Functions, Exponents and Logarithms45m

Week

Week 4

3 hours to complete

Introduction to Probability Theory

This module introduces the vocabulary and notation of probability theory – mathematics for the study of outcomes that are uncertain but have predictable rates of occurrence.

3 hours to complete

8 videos (Total 66 min), 4 readings, 4 quizzes

8 videos

Probability Definitions and Notation7m

Joint Probabilities6m

Permutations and Combinations12m

Using Factorial and “M choose N”6m

The Sum Rule, Conditional Probability, and the Product Rule8m

Bayes’ Theorem (Part 1)10m

Bayes’ Theorem (Part 2)5m

The Binomial Theorem and Bayes Theorem8m

4 readings

A note about the video lectures in this lesson3m

Feedback10m

4 practice exercises

Practice quiz on Probability Concepts25m

Practice quiz on Problem Solving25m

Practice quiz on Bayes Theorem and the Binomial Theorem25m

Probability (basic and Intermediate) Graded Quiz50m

Reviews

4.5

2440 reviews

5 stars
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4 stars
28.02%
3 stars
5.77%
2 stars
1.60%
1 star
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TOP REVIEWS FROM DATA SCIENCE MATH SKILLS

by SPMay 29, 2020

Great refresher and primer. The section on Probability and Bayes Theory needs a lot more support material (video and notes) as it can get tricky and abstract, especially when doing the quizzes.

by KCOct 20, 2020

Weeks 1-3 were quality. Week 4 was a little sloppy in the instructional videos and less clear than in Weeks 1-3. Overall, concise refresher of various mathematical subjects useful to data science.

by DBJun 6, 2020

This course was very easy to understand, though I think week 4's context required more examples. Overall, it was a concise course for brushing up basic math skills required for data science.

by NEJan 3, 2021

I want to thank my educators for this wonderful job that they are doing.i have learn so much from you guys and i want to thank you all for your kind support in this data science maths skills.

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, 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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

More questions? Visit the Learner Help Center.

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Data Science Math Skills

Data Science Math Skills

About this Course

Skills you will gain