Название: Introduction to Quantitative Social Science with Python
Автор: Weiqi Zhang, Dmitry Zinoviev
Издательство: CRC Press
Серия: The Python Series
Год: 2025
Страниц: 356
Язык: английский
Формат: pdf (true), epub
Размер: 13.1 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.

The structure of the book caters to readers with diverse technical backgrounds. A unique feature of the book is its inclusion of two tracks: the “executive track” and the “technical track.” The executive track focuses on conceptual aspects and methodologies of data analysis. Unlike traditional data analysis textbooks that often focus on formulas, our approach prioritizes clarity and understanding over complex mathematical formulas. We avoid presenting mathematical formulas in the chapters to make the content accessible to non-technical readers, allowing them to grasp the conceptual foundations without being hindered by complex mathematical notation. In contrast, the “technical track” focuses on the practical application of Python for the analyses discussed in the executive track.

The dual-track structure offers flexibility to readers, allowing them to tailor their reading experience based on their needs and familiarity with the subject matter. Those inclined to understand conceptual aspects first can progress through the executive track, followed by corresponding chapters in the technical track. Alternatively, readers interested in the conceptual aspects alone can stay on the executive track, while experienced data analysts eager to explore Python could delve directly into the technical track. This adaptable structure ensures that readers can optimize their learning journey according to their preferences and requirements.