Introduction to Probability for Data Science by Stanley H. Chan
Introduction to Probability for Data Science - 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
What You Will Learn in Introduction to Probability for Data Science
"Introduction to Probability for Data Science" by Stanley H. Chan provides a focused and practical introduction to "probability theory" tailored for data science applications. The book covers core concepts such as "random variables", "distributions", expectation, and variance, helping readers build a strong foundation for understanding uncertainty in data-driven contexts. Chan emphasizes clarity and intuition, making probabilistic ideas accessible to students and professionals in data science, computer science, and applied mathematics.
The text bridges theory and application, showing how probability underlies key areas such as statistical inference, machine learning, and decision-making. Real-world examples, exercises, and practical scenarios illustrate how probabilistic methods are applied to analyze datasets, model uncertainty, and interpret results. The book also introduces "conditional probability", helping readers handle dependent events and probabilistic reasoning in complex systems.
Highly suitable for both classroom use and self-study, "Introduction to Probability for Data Science" equips readers with the tools to tackle real-world data challenges. Its combination of rigorous theory, applied examples, and data-focused exercises prepares students for advanced topics like stochastic modeling, predictive analytics, and machine learning. By connecting probability concepts directly to data science problems, Chan’s book serves as a practical guide for understanding and applying "probabilistic methods" in modern analytics.
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 American academic and engineer who earned his "B.Eng. in Electrical Engineering from the University of Hong Kong" and his "Ph.D. in Electrical Engineering from UC?San Diego". He completed postdoctoral work at "Harvard University" and is now an associate professor at "Purdue University", specializing in probability and data-driven engineering. Chan’s expertise includes "probability for data science", computational photography, image processing, and "machine learning". His textbook "Introduction to Probability for Data Science" combines theory with practical examples, helping students apply probabilistic methods to real-world problems in data science and engineering.
Read or Downloadable Introduction to Probability for Data Science
Free Regression Analysis Books PDF | Statistical Learning Resources