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

Word count: 552 words
Chapters: 9
Series: Data Analysis and Decision Technology Series
ISBN: 9787111627760

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Summary

This book explains, from a hands-on perspective, how to use Python to perform data analysis and data mining, and how to bring the results into the daily operations of internet companies. It has nine chapters, logically divided into two parts. Part One (Chapters 1–4) covers data analysis and mining: Chapter 1 introduces the basic concepts, common tools, and components of Python and data-driven operations, and uses a small sales-forecasting case to walk through the whole workflow; Chapter 2 maps out data sources—data files, databases, APIs, streaming data, and public data—and the acquisition and parsing of structured and unstructured data (web pages, text, images, video, audio); Chapter 3 distills ten categories of data preprocessing experience, covering data cleaning, flag conversion, feature dimensionality reduction, sample imbalance, collinearity and correlation, standardization, and discretization; Chapter 4 covers analysis and mining methods organized around clustering, regression, classification, association, anomaly detection, time series, path analysis, funnel analysis, attribution analysis, and heat maps. Part Two (Chapters 5–9) covers data-driven operations, the core of the book, addressing member operations, product operations, traffic operations, and content operations. Each is structured around basic knowledge, key metrics, application scenarios, analytical models, analysis tips, hard truths, and two integrated cases, and the book closes by discussing how to increase the value of data-driven operations. Case data and source code with Chinese comments are provided so readers can reproduce the analyses themselves.

Editor's review

市面上讲 Python 数据分析的书很多，讲运营的书也不少，但真正把两端接起来的很少。这本书的价值恰恰在“接”这一环：前半部分把数据获取、预处理、建模的常用技术梳理成可复用的经验条目，后半部分直接进入会员、商品、流量、内容四个真实运营场景，每个场景都给出指标口径、分析模型、小技巧和两个完整案例，读者能清楚看到某个算法为什么会出现在这个业务问题上。第 2 版把代码升级到较新的 Python 版本，内容也做了大幅补充，共覆盖 50 余个数据工作流知识点、14 个分析挖掘主题和 8 个综合性案例，篇幅扎实但不空泛。作者宋天龙有多年一线数据团队与网站分析服务经验，行文偏实战口吻，少见教科书式的推导，更像一位资深从业者把自己踩过的坑摊开来讲。对于想把“会写 Python”变成“能用数据推动业务”的人来说，这是一本可以放在手边反复翻的技术与业务双语手册。

Who it's for

A good fit for

已掌握 Python 基础语法，想进一步用于业务分析的数据分析初学者
电商、内容平台等互联网公司的运营、增长与用户研究岗位从业者
需要把数据结论转化为运营动作的产品经理与业务负责人
统计、商务分析、大数据相关专业的高年级本科生与研究生

Not a good fit for

完全没有编程基础、希望从零学 Python 语法的读者
只想学机器学习算法推导与理论证明的研究者
希望立刻获得可直接套用的自动化运营系统源码的读者

Reading tips

先通读第 1 章与第 2 章打通数据获取流程，再按自己的业务场景挑选第 5～9 章对应章节精读。
边读边下载书中案例数据与源码动手复现，把示例换成自己手上的真实数据效果最好。
阅读第 3、4 章时建立一份自己的方法速查表，记录每类预处理与分析方法的适用前提。
运营岗读者可先跳读业务章节理解指标体系，再回头补技术部分，避免被代码卡住。

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
Chapter 4 Data Analysis and Mining
Chapter 5 Member Operations
Chapter 6 Product Operations
Chapter 7 Traffic Operations
Chapter 8 Content Operations
Chapter 9 Methods for Increasing the Value of Data-Driven Operations

FAQ

这本书第 1 版和第 2 版有什么区别？

第 2 版将示例代码升级到较新的 Python 版本，并对内容做了大幅补充和优化，案例数量与知识点更丰富，篇幅增至 549 页左右。选购时建议优先考虑第 2 版。

没有运营经验，只学 Python 能读懂吗？

技术部分可以读懂，但第 5～9 章大量涉及会员、商品、流量、内容的业务指标与场景，缺少业务背景时理解会打折。建议边读边补充相关行业的基础运营知识。

书里的代码和数据从哪里获取？

书中案例数据和带中文注释的源代码可下载用于实操；第 2 版还提供了作者联系方式与读者交流渠道，便于获取实时答疑，具体获取方式以书内说明为准。

这本书适合当教材系统学习吗？

它可以作为项目导向的参考书或课程辅助读物，但知识组织偏经验条目与案例，不是按教学大纲循序渐进的教材。系统教学仍需搭配专门的数据分析基础教材。

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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.