Weiqi Zhang, Dmitry Zinoviev

Introduction to Quantitative Social Science with Python (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 356
• Erscheinungstermin: 1. November 2024
• Englisch
• ISBN-13: 9781040148310
• Artikelnr.: 72284460

Autorenporträt

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.

Inhaltsangabe

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

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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