1st Edition
Introduction to Quantitative Social Science with Python
Part 1: “Executive Track”
1. Introduction to Data Analysis in Social Science
2. Data Collection and Cleaning
3. Descriptive and Exploratory Analysis
4. Causality and Hypothesis Testing
5. Linear Regression Analysis
6. Classification
7. Complex Network Analysis
8. Text As Data
Part 2: “Technical Track”
9. Python Programming Fundamentals
10. Data Collection and Cleaning
11. Condition Checking and Descriptive and Exploratory Analysis
12. Loops and Hypothesis Testing
13. User-Defined Functions and Regression Analysis
14. Generators and Classification
15. More Generators and Network Analysis
16. Sets. Text as Data
Conclusion
A. Solutions to Select Exercises
Bibliography
Biography
Weiqi Zhang is an Associate Professor at Suffolk University. He teaches courses on political science and data analytics, and he is passionate about bridging social sciences and data science.
Dmitry Zinoviev is a Professor of Computer Science at Suffolk University. His academic interests include computer modeling and simulation, complex networks, and the integration of computational methods into traditionally non-quantitative fields such as the humanities and social sciences.
"One of the standout features of this book is its innovative dual-track layout, which balances the foundational theories with practical programming skills, catering to a wide range of readers. The executive track focuses on providing intuitive and conceptual explanations of statistical concepts using relevant examples, offering a high-level understanding of essential social science methods. On the other hand, the technical track illustrates these statistical methods through hands-on Python programming. By making advanced data-driven analysis feel approachable without losing depth, the book will benefit students and researchers from non-technical fields. I believe that this book will be a valuable resource for those who are stepping into quantitative social science analysis."
-Salil Koner, Journal of the American Statistical Association, March 2026.
