Introduction to Quantitative Social Science with Python

AvWeiqi Zhang,Dmitry Zinoviev

Inbunden, Engelska, 2024

Del i serien Chapman & Hall/CRC The Python Series

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2024-11-01
Mått:156 x 234 x 24 mm
Vikt:453 g
Format:Inbunden
Språk:Engelska
Serie:Chapman & Hall/CRC The Python Series
Antal sidor:332
Förlag:Taylor & Francis Ltd
ISBN:9781032354606

Utforska kategorier

Referensverk och tvärvetenskap inom Samhälle och politik
Sociologi inom Samhälle och politik
Programspråk inom Data och IT

Mer om författaren

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.

Recensioner i media

"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.

Innehållsförteckning

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