Introduction to Probability for Data Science

This introductory textbook in undergraduate probability emphasizes the inseparability between data (computing) and probability (theory) in our time. It examines the motivation, intuition, and implication of the probabilistic tools used in science and engineering:

1. Motivation: In the ocean of mathematical definitions, theorems, and equations, why should we spend our time on this particular topic but not another?
2. Intuition: When going through the deviations, is there a geometric interpretation or physics beyond those equations?
3. Implication: After we have learned a topic, what new problems can we solve?

Table of Contents

1. Mathematical Background
2. Probability
3. Discrete Random Variables
4. Continuous Random Variables
5. Joint Distributions
6. Sample Statistics
7. Regression
8. Estimation
9. Confidence and Hypothesis
10. Random Processes

About the Author

Stanley H. Chan is an associate professor of electrical and computer engineering, and an associate professor of statistics, at Purdue University, West Lafayette. His research areas include computational photography, image processing, and machine learning. At Purdue, he teaches undergraduates probability and graduates machine learning. He is a recipient of Purdue University College of Engineering Exceptional Early Career Teaching Award, the Ruth and Joel Spira Outstanding Teaching Award, Purdue Teaching for Tomorrow Fellow, among other awards.