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Introduction to Quantitative Social Science with Python. Pythonを用いた量的社会科学入門
・ISBN 978-1-032-35460-6 hard GB£ 171.99
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・ISBN 978-1-032-35659-4 paper GB£ 55.99
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電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-1-003-32789-9
| 著者・編者 | Zhang, Weiqi / Zinoviev, Dmitry, |
|---|---|
| シリーズ | Chapman & Hall/CRC The Python Series |
| 出版社 | (Chapman & Hall / CRC, US) |
| 出版年月 | 2024.11 |
| ページ数 | 332 pp. |
| 言語 | ENG |
| ニュース番号 | ＜727-16 727-187＞ |
解説
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.