About this course: 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!

Who is this class for: This course is for anyone who has basic math skills, but is interested in learning or relearning algebra or pre-calculus so they can be successful in other data science math courses.

Created by: Duke University

Taught by: Daniel Egger, Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University
Taught by: Paul Bendich, Assistant research professor of Mathematics; Associate Director for Curricular Engagement at the Information Initiative at Duke
Mathematics

Level	Beginner
Commitment	Four weeks, 3-5 hours per week.
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.4 stars Average User Rating 4.4See what learners said

Syllabus

WEEK 1

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

1 video, 2 readings

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 appl...

10 videos, 4 readings, 3 practice quizzes

Graded: Graded quiz on Sets, Number Line, Inequalities, Simplification, and Sigma Notation

WEEK 2

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 ...

8 videos, 3 readings, 2 practice quizzes

Graded: Graded quiz on Cartesian Plane and Types of Function

WEEK 3

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 probl...

7 videos, 3 readings, 2 practice quizzes

Graded: Graded quiz on Tangent Lines to Functions, Exponents and Logarithms

WEEK 4

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. We start with the basic definitions and rules of probability, including the probabil...

8 videos, 4 readings, 3 practice quizzes

Graded: Probability (basic and Intermediate) Graded Quiz

FAQs

How It Works

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Creators

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.

Ratings and Reviews

Rated 4.4 out of 5 of 217 ratings

a rewarding journey

It was a good course for a beginner.But I think the proportion of the course allocated to probability is much less than it deserve.:)

Great Primer for Data Science Math!

Although I previously studied these things I have long forgotten them or they WAY I was taught them previously was incompatible with Data Science in general. Do this course first if you want to start data science and your maths needs some work.

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