From Social Science to Data Science
eBook - ePub

From Social Science to Data Science

Key Data Collection and Analysis Skills in Python

  1. 400 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

From Social Science to Data Science

Key Data Collection and Analysis Skills in Python

Book details
Table of contents
Citations

About This Book

From Social Science to Data Science is a fundamental guide to scaling up and advancing your programming skills in Python. From beginning to end, this book will enable you to understand merging, accessing, cleaning and interpreting data whilst gaining a deeper understanding of computational techniques and seeing the bigger picture. With key features such as tables, figures, step-by-step instruction and explanations giving a wider context, Hogan presents a clear and concise analysis of key data collection and skills in Python.

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Yes, you can access From Social Science to Data Science by Bernie Hogan in PDF and/or ePUB format, as well as other popular books in Sozialwissenschaften & Wissenschaftliche Forschung & Methodik. We have over one million books available in our catalogue for you to explore.

Information

Publisher
SAGE Publications Ltd
Year
2022
ISBN
9781529738070
Edition
1
Topic
Sozialwissenschaften
Subtopic
Wissenschaftliche Forschung & Methodik

Table of contents

  1. Cover
  2. Half Title
  3. Publisher Note
  4. Title Page
  5. Copyright Page
  6. Brief Contents
  7. Contents
  8. Illustration List
  9. Discover This Textbook’s Online Resources!
  10. About the Author
  11. Acknowledgements
  12. Prologue
  13. Part I Thinking Programmatically
  14. 1 Introduction: Thinking of life at scale
  15. 2 The Series: Taming the Distribution
  16. 3 The DataFrame: Python’s tabular format
  17. Part II Accessing and Converting Data
  18. 4 File types: Getting data in
  19. 5 Merging and grouping data
  20. 6 Accessing data on the World Wide Web using code
  21. 7 Accessing APIs, INCLUDING TWITTER AND REDDIT
  22. Part III Interpreting data: Expectations versus observations
  23. 8 Research questions
  24. 9 Visualising expectations: Comparing statistical tests and plots
  25. Part IV Social Data Science in Practice: Four Approaches
  26. 10 Cleaning data for socially interesting features
  27. 11 Introducing natural language processing: Cleaning, summarising, and classifying text
  28. 12 Introducing time-series data: Showing periods and trends
  29. 13 Introducing network analysis: Structuring relationships
  30. 14 Introducing geographic information systems: Data across space and place
  31. 15 Conclusion: there (to data science) and back again (to social science)
  32. References
  33. Index