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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

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

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

4.47/5 (3,161 Ratings)

Daniel Egger

Executive in Residence and Director, Center for Quantitative Modeling

Pratt School of Engineering, Duke University

1,035,065 Learners

8 Courses

Paul Bendich

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

Mathematics

372,769 Learners

2 Courses

Offered by

Duke University

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Syllabus - What you will learn from this course

Content Rating96%(48,502 ratings)

Week1

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 Vocabulary9mSets - Medical Testing Example11mSets - Venn Diagrams7mNumbers - The Real Number Line9mNumbers - Less-than and Greater-than6mNumbers - Algebra With Inequalities10mNumbers - Intervals and Interval Notation7mSigma Notation - Introduction to Summation9mSigma Notation - Simplification Rules7mSigma Notation - Mean and Variance12m

4 readings

A note about the video lectures in this lesson3mA note about the video lectures in this lesson10mA note about the video lectures in this lesson10mFeedback10m

4 practice exercises

Practice quiz on Sets15mPractice quiz on the Number Line, including Inequalities25mPractice quiz on Simplification Rules and Sigma Notation20mGraded quiz on Sets, Number Line, Inequalities, Simplification, and Sigma Notation35m

Week2

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 Points7mCartesian Plane - Distance Formula10mCartesian Plane - Point-Slope Formula for Lines8mCartesian Plane: Slope-Intercept Formula for Lines7mFunctions - Mapping from Sets to Sets7mFunctions - Graphing in the Cartesian Plane11mFunctions - Increasing and Decreasing Functions10mFunctions - Composition and Inverse10m

3 readings

A note about the video lectures in this lesson3mA note about the video lectures in this lesson3mFeedback10m

3 practice exercises

Practice quiz on the Cartesian Plane15mPractice quiz on Types of Functions20mGraded quiz on Cartesian Plane and Types of Function40m

Week3

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 Point10mTangent Lines - The Derivative Function9mUsing Integer Exponents7mSimplification Rules for Algebra using Exponents11mHow Logarithms and Exponents are Related12mThe Change of Base Formula4mThe Rate of Growth of Continuous Processes11m

3 readings

A note about the video lectures in this lesson10mA note about the video lectures in this lesson3mFeedback10m

3 practice exercises

Practice quiz onTangent Lines to Functions10mPractice quiz on Exponents and Logarithms40mGraded quiz on Tangent Lines to Functions, Exponents and Logarithms45m

Week4

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 Notation7mJoint Probabilities6mPermutations and Combinations12mUsing Factorial and “M choose N”6mThe Sum Rule, Conditional Probability, and the Product Rule8mBayes’ Theorem (Part 1)10mBayes’ Theorem (Part 2)5mThe Binomial Theorem and Bayes Theorem8m

4 readings

A note about the video lectures in this lesson3mA note about the video lectures in this lesson3mA note about the video lectures in this lesson3mFeedback10m

4 practice exercises

Practice quiz on Probability Concepts25mPractice quiz on Problem Solving25mPractice quiz on Bayes Theorem and the Binomial Theorem25mProbability (basic and Intermediate) Graded Quiz50m

Reviews

4.5

2499 reviews

5 stars
63.91%
4 stars
27.91%
3 stars
5.73%
2 stars
1.58%
1 star
0.85%

TOP REVIEWS FROM DATA SCIENCE MATH SKILLS

by AAOct 19, 2020

This is a great course, many things are basic but I have learned those a long day ago. Some explanations are from different perspective than conventional. However, definitely a great course.

by SRApr 28, 2020

It was a very good opportunity to go through the course, and the content was good. I can say I definitely learnt a lot in this course. Thanks team and kudos to great work you guys are doing.

by DBJun 7, 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 PSJul 23, 2017

This is neat little course to revise math fundamentals. I generally find learning probability a little tricky. This course helped me a lot in better understanding Bayes Theorem. Thank you professors.

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