Introduction to Quantitative Social Science with Python (Chapman & Hall/CRC The Python Series)
Introduction to Quantitative Social Science with Python (Chapman & Hall/CRC The Python Series) by Weiqi Zhang, Dmitry Zinoviev
English | November 1, 2024 | ISBN: 1032356596 | 356 pages | MOBI | 5.79 Mb
English | November 1, 2024 | ISBN: 1032356596 | 356 pages | MOBI | 5.79 Mb
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.
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