1. Introduction
2. First Simulations, Visualizations, and Statistical Tests
3. First Visualizations and Statistical Tests with Real Data
4. Introduction to Probability
5. Null Hypothesis Tests
6. Conditional Probability, Dependence, and Independence
7. Introduction to Bayesian Methods
8. Random Variables
9. Expected Value, Parameter Estimation, and Hypothesis Tests on Sample Means
10. Decision Making with Observations from Continuous Distributions
11. Categorical Data, Tests for Dependence, and Goodness of Fit for Discrete Distributions
12. Multidimensional Data: Vector Moments and Linear Regression
13. Working with Dependent Data in Multiple Dimensions
Biography
John M. Shea, PhD is a Professor in the Department of Electrical and Computer Engineering at the University of Florida, where he has taught classes on stochastic methods, data science, and wireless communications for over 20 years. He earned his PhD in Electrical Engineering from Clemson University in 1998 and later received the Outstanding Young Alumni award from the Clemson College of Engineering and Science. Dr. Shea was co-leader of Team GatorWings, which won the Defense Advanced Research Project Agency’s (DARPA’s) Spectrum Collaboration Challenge (DARPA's fifth Grand Challenge) in 2019. He received the Lifetime Achievement Award for Technical Achievement from the IEEE Military Communications Conference (MILCOM) and is a two-time winner of the Ellersick Award from the IEEE Communications Society for the Best Paper in the Unclassified Program of MILCOM. He has been an editor for IEEE Transactions on Wireless Communications, IEEE Wireless Communications magazine, and IEEE Transactions on Vehicular Technology.
"Foundations of Data Science with Python, by John M. Shea, provides a comprehensive and modern introduction of data science. The book illustrates different aspects of working with data computationally using the programming language Pyhon. It incoporates many trendy and practical real data examples, such as COVID-19 datasets, in its discussion, while at the same time retaining mathematical rigor by covering topics on probability and random variables, among others. Overall, the book is beautifully written and offers sufficient coding support for Python beginners to follow along. It is a valuable addition to the shelf for anyone who wishes to gain basic knowledge of data science. In addition, it is an excellent choice as a textbook for a first course in data science." - Qing Wang, The American Statistician
