Module 2: Math & Statistics
Overview
Welcome to the Math course for the EDSP Mentoring Program!
Data Science involves many areas of mathematics from statistics, calculus, linear algebra, and more. The material in this module is around statistics and probability.
While we won’t get into vector calculus or continuous optimization, this knowledge will help serve as a foundation as you continue into the later modules.
Before you begin
We will be using the Statistics and Probability course from Khan Academy. There are a total of 15,200 Mastery Points availabile that are earned as you progress.
What you’ll learn
- Analyzing Categorical and Quantitative Data
- Data Distributions
- Probability
- Confidence Intervals
- Significance Tests
Topic Kickoff
| Resources | Links |
|---|---|
| Recording | Recording |
| Presentation | Presentation |
Table of Contents
| Units | Possible Mastery Points | Priority |
|---|---|---|
| Analyzing Categorical Data | 1,300 | X |
| Displaying and Comparing Quantitative Data | 1,200 | X |
| Summarizing Quantitative Data | 1,700 | X |
| Modeling Data Distributions | 900 | |
| Exploring Bivariate Numerical Data | 1,300 | X |
| Study Design | 900 | |
| Probability | 1,600 | |
| Counting, Permutations, and Combinations | 500 | |
| Random Variables | 2,100 | X |
| Sampling Distributions | 700 | |
| Confidence Intervals | 800 | |
| Significance Tests | 1,500 | X |
| Two-Sample Inference for the Difference between Groups | 0 | |
| Inference of Categorical Data | 700 | X |
| Advanced Regression | 0 | |
| Analysis of Variance (ANOVA) | 0 |
Test your knowledge
Once you have completed the above coursework, test your knowledge with the course challenge!
The challenge is 30 questions and should take around 30-45 minutes.
Additional / Optional Resources
Here are some great, free PDF textbooks covering additional, more in-depth mathematical topics:
- Mathematics for Machine Learning: covers foundations of linear algebra, matrix decompositions, vector calculus, and then ML algorithms using these foundations. Includes exercises and tutorials in Python
- Mathematics for ML: covers linear algebra, calculus, optimization, and probability. Shorter than above book, but no exercises.
- Intro to Probability and Statistics: deeply covers probability and stats. created by professor at Purdue University and includes Python exercises, video lectures, and slides
Here are some additional Khan Academy Courses:
Before the above calculus classes, consider these courses: