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.

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## Data Science Math Skills

Duke University## About this Course

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

Try Coursera for Business## Skills you will gain

- Bayes' Theorem
- Bayesian Probability
- Probability
- Probability Theory

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

Try Coursera for Business## Offered by

### Duke University

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

## Syllabus - What you will learn from this course

**18 minutes to complete**

## Welcome to Data Science Math Skills

This short module includes an overview of the course's structure, working process, and information about course certificates, quizzes, video lectures, and other important course details. Make sure to read it right away and refer back to it whenever needed

**18 minutes to complete**

**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**

**3 hours to complete**

## Functions and Graphs

This module builds vocabulary for graphing functions in the plane. In the first lesson, "Descartes Was Really Smart," you will get to know the Cartesian Plane, measure distance in it, and find the equations of lines. The second lesson introduces the idea of a function as an input-output machine, shows you how to graph functions in the Cartesian Plane, and goes over important vocabulary.

**3 hours to complete**

**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**

**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**

## Reviews

- 5 stars63.71%
- 4 stars28.06%
- 3 stars5.80%
- 2 stars1.59%
- 1 star0.82%

### TOP REVIEWS FROM DATA SCIENCE MATH SKILLS

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.

The content of the course as a whole is interesting as a good synthesis of basic skills, even though the content of the weeks 3 and 4 lacks of clarity compared to the content of the weeks 1 and 2.

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.

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.

## Frequently Asked Questions

When will I have access to the lectures and assignments?

What will I get if I purchase the Certificate?

Is financial aid available?

Will I receive a transcript from Duke University for completing this course?

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