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.
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 |
- Video: Welcome to Data Science Math Skills
- Reading: Course Information
- Reading: Weekly feedback surveys
- 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
- 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
- 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
- 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
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Its nice course to brush up basic maths. It would be helpful if you can add more topics which are required to complete data science like matrices and other concepts
As it is now, the course is a much better resource for reviewing the material (which was fine for me as it was what I was trying to do) than for learning it first time. It would be much better if it had more of the same, which is why I am giving it 4 stars instead of 5. In my opinion, it is too brief; I hope to see a part 2 expanding on the material provided here. Many of the topics mentioned, and they really were mentioned more than really taught, should have been talked about in more detail. I've completed the whole course in about 4-6 hours over 2.5 days. It is a good attempt, but it is hardly a sufficient preparation for the field of Data Science; students looking to take the course should be aware of this.
TLDR: A nice and brief overview of many important concepts (sadly, missing linear algebra) which lay the mathematical foundation for getting into Data Science. Needs to be expanded upon.
Excelent course! I am studying at the Data Science Math Skills to fill some math gaps, and also doing the Data Scientist Toolbox . All of them are really great courses. Enjoy!
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SB
Course is simple yet difficult. It explains most of the concepts in simple way but the assignments are pretty hard to solve :) overall makes perfect foundation and provide necessary skills for data Science. More focus on derivatives will help.