Python Data Analysis and Data-Driven Operations (2nd Edition)

Name: Python Data Analysis and Data-Driven Operations (2nd Edition)
ISBN: 9787111627760

宋天龙China Machine Press2019

3.7

Chapters: 11
Series: Data Analysis and Decision Technology Series
ISBN: 9787111627760

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Summary

This is the second edition of Python Data Analysis and Data-Driven Operations, written by big data technology expert Song Tianlong and published by China Machine Press in 2019 as part of the Data Analysis and Decision Technology Series. Unlike data analysis books that cover only tools and algorithms, this book connects Python technology with real corporate data scenarios and emphasizes that analysis must serve operational decisions. The book has nine chapters, logically divided into two parts. Part One (Chapters 1–4) covers Python data analysis and mining: Chapter 1 introduces Python's role in data-driven operations, common tools, and components; Chapter 2 explains how to obtain and read data from files, MySQL, MongoDB, APIs, web pages, and channels such as images, video, and voice; Chapter 3 summarizes preprocessing experience in data cleaning, flag conversion, dimensionality reduction, sample imbalance, sampling, collinearity, correlation, standardization, and discretization; Chapter 4 covers analytical methods including clustering, regression, classification, association, anomaly detection, time series, and path, funnel, attribution, and heatmap analysis. Part Two (Chapters 5–9) is the core of the book and is organized around four operational themes: members, products, traffic, and content. Each theme includes basic concepts, key metrics, application scenarios, analytical models, analysis tips, experiential reminders, and two comprehensive cases. The book ends by discussing how to write analysis reports and how to increase the value of data-driven operations. The cases come with data and source code with Chinese comments so readers can reproduce them.

Editor's review

很多Python数据分析书讲到模型调参就结束了，而真正让数据产生价值的是“把结论落到业务里”。这本书的稀缺之处在于，它把技术链路和运营场景绑在一起：从数据获取、清洗、建模，一直讲到会员、商品、流量、内容四大主题的指标体系与案例落地。作者宋天龙有十余年一线数据项目经验，书中总结的是踩过坑之后的经验，比如什么样的数据异常不能直接丢弃、相关系数低是否等于不相关、聚类为什么常常是预处理的中间步骤。第2版把Python升级到较新版本，篇幅扩充明显，案例算法覆盖RFM、营销响应预测、销售预测、异常检测、KMeans广告效果聚类、LDA主题挖掘、贝叶斯增量文本分类等，并且提供案例数据与中文注释代码。对已经会写pandas、却不知道怎么把分析结果讲给业务方听的人来说，这本书能补上“从分析到决策”这一环。

Who it's for

A good fit for

掌握Python基础语法、希望把技能用到真实业务场景的数据分析初学者
电商、零售、互联网行业需要做用户、商品、流量分析的数据分析师
运营、市场岗中需要自己动手做数据分析和报表的从业者
希望系统了解会员、商品、流量、内容四大运营指标体系的管理者

Not a good fit for

完全没有Python和统计基础、希望从零学编程的读者
只想学习机器学习算法推导和数学原理的研究型读者
需要最新版Python、最新大模型工具链教程的读者，本书内容停留在2019年前后的技术栈

Reading tips

先通读第1章了解工具与整体框架，再按自己所在行业挑选第5—8章中的对应主题精读。
边读边运行随书案例代码，重点观察每个案例从数据到结论、再到业务建议的完整链条。
第3、4章的“经验”部分可当作检查清单，遇到实际项目问题时回来对照排查。
把书中的指标定义整理成自己的业务指标字典，便于在工作中与运营同事对齐口径。

About the publisher

China Machine Press China · Founded in 1950

In addition to science and technology and professional publishing, the books in our catalog from China Machine Press also reflect a sustained focus on psychology, computing, management, finance and economics, and social sciences. In psychology, the catalog includes works by C. G. Jung and introductions to analytical psychology, as well as applied topics such as stress and health, and addiction and healing. In computing, it features Structure and Interpretation of Computer Programs (JavaScript Edition). Management and finance titles include Administrative Behavior (4th Edition), The Price of Inequality (Collector's Edition), and This Time Is Different: Eight Centuries of Financial Folly (Commemorative Edition). In the philosophy and history of science, there is The Worldview: The Third Edition. Relevant authors include C. G. Jung, William McGuire, Sonu Shamdasani, and Gabor Maté. Public information is limited; the above summarizes the publisher's profile based solely on the books in our catalog.

Chapter 1 Python and Data-Driven Operations
Chapter 2 Data Sources for Data-Driven Operations
Chapter 3 Data Preprocessing Experience You Must Know for Data-Driven Operations
Chapter 4 Avoiding the "Big Pitfalls" of Operational Data Analysis and Mining
Chapter 5 Member Data-Driven Operations
Chapter 6 Product Data-Driven Operations
Chapter 7 Traffic Data-Driven Operations
Chapter 8 Content Data-Driven Operations
Chapter 9 The Ultimate Secret of Data-Driven Operational Analysis
Appendix A Public Datasets
Appendix B Python Data Toolbox

FAQ

这本书有第1版和第2版，应该买哪个？

建议读第2版。第2版（2019年，ISBN 9787111627760）把Python升级到较新版本，内容做了大幅补充和优化，篇幅比第1版增加三成以上，案例也更完整；第1版为2017年出版，ISBN 9787111584605。

零基础可以直接读这本书吗？

不太建议。本书默认读者已会基本的Python语法和pandas操作，重点在数据工作流和业务落地，而非入门教学。零基础读者可先补Python与统计基础，再回来读第3、4章和第5—8章。

书中的案例代码和数据怎么获取？

作者提供了随书案例数据和带中文注释的源代码供下载，书中也留有作者联系方式以便读者交流答疑。由于出版时间较早，运行代码时可能需按当前Python版本调整部分库的接口。

这本书更偏技术还是偏业务？

两者兼顾，但落脚点在业务。前半部分讲数据获取、预处理和分析方法，后半部分按会员、商品、流量、内容四大主题讲指标体系、分析模型和案例，适合想补齐业务视角的技术读者。

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About the author

宋天龙 Author

Public information about Song Tianlong is limited, and his nationality, dates of birth and death, and educational background cannot be confirmed. Judging from the three works held in the collection, his writing centers on computer technology fields such as big data, artificial intelligence, and programming design, with particular attention to the practical application of AIGC technologies. His representative works include AIGC-Assisted Data Analysis and Mining: Methods and Practice Based on ChatGPT, AIGC-Assisted Data Analysis and Data-Driven Operations: Scenario-Based Solutions and Case Studies, and Full-Stack Development and Application of Python Big Data Architecture. These works show that he excels at combining generative tools such as ChatGPT with data analysis, data-driven operations, and full-stack development, pairing methodological explanations with scenario-based cases for technical staff and business readers seeking to use AI to improve data work efficiency. Overall, his writing leans toward practical application and scenario-based solutions and offers some reference value in the categories of general computing and artificial intelligence.