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 |
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
- Video: Welcome to Data Science Math Skills
- Reading: Course Information
- Reading: Weekly feedback surveys
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.
10 videos, 4 readings, 3 practice quizzes
- Reading: A note about the video lectures in this lesson
- Video: Sets - Basics and Vocabulary
- Video: Sets - Medical Testing Example
- Video: Sets - Venn Diagrams
- Practice Quiz: Practice quiz on Sets
- Reading: A note about the video lectures in this lesson
- Video: Numbers - The Real Number Line
- Video: Numbers - Less-than and Greater-than
- Video: Numbers - Algebra With Inequalities
- Video: Numbers - Intervals and Interval Notation
- Practice Quiz: Practice quiz on the Number Line, including Inequalities
- Reading: A note about the video lectures in this lesson
- Video: Sigma Notation - Introduction to Summation
- Video: Sigma Notation - Simplification Rules
- Video: Sigma Notation - Mean and Variance
- Practice Quiz: Practice quiz on Simplification Rules and Sigma Notation
- Reading: Feedback
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 function as an input-output machine, shows you how to graph functions in the Cartesian Plane, and goes over important vocabulary.
8 videos, 3 readings, 2 practice quizzes
- Reading: A note about the video lectures in this lesson
- Video: Cartesian Plane - Plotting Points
- Video: Cartesian Plane - Distance Formula
- Video: Cartesian Plane - Point-Slope Formula for Lines
- Video: Cartesian Plane: Slope-Intercept Formula for Lines
- Practice Quiz: Practice quiz on the Cartesian Plane
- Reading: A note about the video lectures in this lesson
- Video: Functions - Mapping from Sets to Sets
- Video: Functions - Graphing in the Cartesian Plane
- Video: Functions - Increasing and Decreasing Functions
- Video: Functions - Composition and Inverse
- Practice Quiz: Practice quiz on Types of Functions
- Reading: Feedback
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 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.
7 videos, 3 readings, 2 practice quizzes
- Reading: A note about the video lectures in this lesson
- Video: Tangent Lines - Slope of a Graph at a Point
- Video: Tangent Lines - The Derivative Function
- Practice Quiz: Practice quiz onTangent Lines to Functions
- Reading: A note about the video lectures in this lesson
- Video: Using Integer Exponents
- Video: Simplification Rules for Algebra using Exponents
- Video: How Logarithms and Exponents are Related
- Video: The Change of Base Formula
- Video: The Rate of Growth of Continuous Processes
- Practice Quiz: Practice quiz on Exponents and Logarithms
- Reading: Feedback
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 probability of two or more events both occurring, the sum rule and the product rule, and then proceed to Bayes’ Theorem and how it is used in practical problems.
8 videos, 4 readings, 3 practice quizzes
- Reading: A note about the video lectures in this lesson
- Video: Probability Definitions and Notation
- Video: Joint Probabilities
- Practice Quiz: Practice quiz on Probability Concepts
- Reading: A note about the video lectures in this lesson
- Video: Permutations and Combinations
- Video: Using Factorial and “M choose N”
- Video: The Sum Rule, Conditional Probability, and the Product Rule
- Practice Quiz: Practice quiz on Problem Solving
- Reading: A note about the video lectures in this lesson
- Video: Bayes’ Theorem (Part 1)
- Video: Bayes’ Theorem (Part 2)
- Video: The Binomial Theorem and Bayes Theorem
- Practice Quiz: Practice quiz on Bayes Theorem and the Binomial Theorem
- Reading: Feedback
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
Great course, many examples, good teachers
Enjoyed the first 3 weeks quite a bit. The probability lectures were not as strong as lectures in Weeks 1 through 3, however. I took the Intuitive Probability course offered by Zurich University to really learn probability well and only thenI was able to pass exams in week 4 of this course.
A great introduction to the basic Mathematics skill needed to start Data Science Career. A must course for does not have the mathematics background required for Data Science.
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Really good course to get a understanding of the basic concepts required to start with data sciences. Teachers are excellent and the quizs and assignments are challenging.