00000nam a2200000 i 4500 UP-1686042739785045117 Buklod 20250421133100.0 m |o d | cr ||||||||||| 250421s2023 cau a b 001 0 eng d 781529707489 paperback 22540304 Belview purchase IR-12971 DLC DSOLAIR rda eng H 62 H64 2023 Hogan, Bernie author. From social science to data science Bernie Hogan. Los Angeles Sage [2023] xxiv, 361 pages illustrations (chiefly color) 26 cm text txt rdacontent unmediated rdamedia volume nc rdacarrier Includes bibliographical references and index. Part I: Thinking Pragmatically--Chapter 1. Introduction --Chapter 2. The Series --Chapter 3. The DataFrame --Part II: Accessing and Converting Data--Chapter 4. File Types --Chapter 5. Merging and Grouping Data --Chapter 6. Accessing the Web --Chapter 7. Accessing APIs --Part III. Interpreting Data: Expectations versus Observations--Chapter 8. Research Questions --Chapter 9. Visualising Expectations --Part IV: Social Data Science in Practice: Four Approaches--Chapter 10. Cleaning Data --Chapter 11. Introducing Natural Language Processing --Chapter 12. Introducing Time Series Data --Chapter 13. Introducing Network Analysis --Chapter 14. Introducing Geographic Information Systems --Chapter 15. Conclusion rom 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 into 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 on key data collection and skills in Python. -- Provided by publisher. Data mining. Python (Computer program language) Social sciences Research Data processing. FO UPD DSOLAIR H 62 H64 2023 Book