1st Edition
Introduction to Quantitative Social Science with Python
Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python.
Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel.
Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.
Key Features:
- Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis.
- Includes comprehensive quantitative methodologies for quantitative social science studies.
- Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience.
- Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.
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.
