Introduction to Probability for Data Science by Stanley H. Chan
Introduction to Probability for Data Science - Table of Contents
- 1. Mathematical Background
- 2. Foundations of Probability
- 3. Discrete Random Variables
- 4. Continuous Random Variables
- 5. Analysis of Joint Distributions
- 6. Sample Statistics
- 7. Linear and Multiple Regression
- 8. Parameter Estimation Methods
- 9. Confidence Intervals and Hypothesis Testing
- 10. Introduction to Random Processes
What You Will Learn in Introduction to Probability for Data Science
Its Book Detail is written as
Introduction to Probability for Data Science by Stanley H. Chan is a groundbreaking university-level textbook specifically designed to bridge classical probability theory with modern data science applications. This accessible text breaks down foundational topics like conditional probability, random vectors, maximum likelihood estimation, and high-dimensional statistics into intuitive, computationally grounded lessons.
Perfect for undergraduate and graduate students in electrical engineering, computer science, statistics, and artificial intelligence, this comprehensive book develops rigorous theory alongside real-world computational workflows. Professor Chan connects essential mathematical abstractions with practical tools used in modern AI—including linear regression, classification algorithms, principal component analysis, and Kalman filtering. Whether analyzing high-dimensional signals or building probabilistic machine learning models, learners will find this structured framework invaluable.
Recognized globally for its visual explanations, practical relevance, and student-focused clarity, it remains one of the best probability for data science books pdf available for study. It systematically balances fundamental theoretical principles with practical algorithmic implementation, making it an essential resource for mastering data science probability with confidence.
Book Details & Specifications
Title:
Introduction to Probability for Data Science by Stanley H. Chan
Publisher:
Michigan Publishing Services
Year:
2021
Pages:
709
Type:
PDF
Language:
English
ISBN-10 #:
1607857464
ISBN-13 #:
978-1607857464
License:
External Educational Resource
Amazon:
Amazon
About the Author: Stanley H. Chan
The author Stanley H. Chan
is an Associate Professor of Electrical and Computer Engineering and Statistics at Purdue University. He received his Ph.D. in Electrical Engineering from the University of California, San Diego, and completed postdoctoral research at Harvard University.
An active researcher in computational imaging, signal processing, and statistical machine learning, Professor Chan is a recipient of the NSF CAREER Award and multiple teaching excellence awards. His work on probabilistic systems and data science pedagogy equips students worldwide with clear, actionable mathematical tools for modern quantitative analysis.
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