Python Data Analysis and Data-Driven Operations

Name: Python Data Analysis and Data-Driven Operations
ISBN: 9787111584605

宋天龙China Machine Press2017

3.7

Word count: 370K words
Chapters: 40
Series: Data Analysis and Decision Technology Series
ISBN: 9787111584605

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Summary

This is a technical book that explains, from a hands-on perspective, how to use Python for data analysis, data mining, and data-driven operations. It has nine chapters, logically divided into two parts. Part One (Chapters 1–4) covers Python data analysis and mining: it first introduces basic concepts of Python and data-driven operations, along with the toolchain—Jupyter, common third-party libraries, database clients, and so on; then systematically explains how to obtain structured and unstructured data (text files, Excel, MySQL, MongoDB, APIs, as well as web pages, text, images, video, and audio) and summarizes practical experience in data cleaning, flag conversion, dimensionality reduction, handling imbalanced samples, conflicts among multiple data sources, sampling, collinearity, correlation analysis, standardization, and discretization; it then presents modeling approaches and common pitfalls around clustering, regression, classification, association, anomaly detection, time series, paths and funnels, attribution, and heatmaps. Part Two (Chapters 5–9) is the core of the book, breaking down four major themes one by one: member operations, product operations, traffic operations, and content operations. Each theme includes basic knowledge, key metrics, application scenarios, analytical models, practical tips, experience-based “home truths,” and two comprehensive cases, such as RFM-based user value analysis and AdaBoost-based marketing response prediction. The book closes by discussing how to increase the value of data-driven operations. Case data and Chinese-annotated source code are provided for download and practice.

Editor's review

市面上讲 Python 数据分析的书很多，但讲完模型之后还能把结果交回到业务中去的书却很少。这本书最大的亮点是“双线结构”：前四章把数据获取、清洗、降维、建模这些基本功讲扎实，而且不走流水账，而是围绕“运营场景里会踩什么坑”来组织；后五章则直接以会员、商品、流量、内容四大运营主题为骨架，每个主题都给指标、给模型、给两个完整案例，读者可以照着把分析从取数一路走到结论与落地建议。作者宋天龙长期在电商与网站分析一线工作，书中处处能看到业务视角的提醒——比如不要被二八法则套住、点击热力图不等于用户偏好、用户不购买不等于流失。这些“大实话”章节是同类书中少见的、真正降低新手误判风险的内容。全书配套案例数据与带中文注释的源码，适合边读边敲。需要注意：本书偏重方法与场景理解，而非 Python 语法入门教学。

Who it's for

A good fit for

已掌握 Python 基础语法、想把它用到真实业务分析中的读者
电商、互联网公司里负责会员、商品、流量、内容运营的从业者
需要从“会跑模型”进阶到“能出业务结论”的数据分析新人
统计学、商务分析、大数据相关专业的高年级本科生与研究生
想系统搭建数据指标体系与分析框架的运营或产品负责人

Not a good fit for

完全没有 Python 或编程基础的零起点读者
只想学 pandas 语法与可视化画图技巧的工具书需求者
期望深入学习机器学习算法推导与理论证明的读者
只做传统线下业务、无网络数据可用的从业者

Reading tips

先通读第 1 章和第 2 章，按书中说明把运行环境、常用库与示例数据配置好，再进入后面的章节。
第 3、4 章建议对照代码逐段运行，把预处理与建模中的“坑”在自己的数据上复现一次。
第 5～9 章可按自己的工作职责挑读，例如做用户增长先读会员运营，再做商品和流量。
把每章的“大实话”和案例注意点整理成自己的检查清单，做分析前对照一遍，能少走很多弯路。

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
1.1 Using Python for Data-Driven Operations
1.2 Python Tools and Components Needed for Data-Driven Operations
1.3 Extended Reading: Python’s OCR and TensorFlow
1.4 The First Data-Driven Operations Analysis Example in Python: Sales Forecasting
Chapter 2 Data Sources for Data-Driven Operations
2.1 Types of Data Sources for Data-Driven Operations
2.2 Using Python to Obtain Operational Data (Text Files, Excel, MySQL, MongoDB, APIs)
2.3 Extended Reading: Reading Unstructured Web Pages, Text, Images, Video, and Audio
Chapter 3 Eleven Data Preprocessing Lessons You Must Know for Data-Driven Operations
3.1 Data Cleaning: Missing Values, Outliers, and Duplicates
3.2 Flag Variable Conversion for Categorical and Ordinal Data
3.3 Dimensionality Reduction
3.4 Imbalanced Sample Class Distribution
3.5 Conflicts Among Multiple Data Sources
3.6 Sampling vs. Full Data
3.7 Collinearity
3.8 Correlation Analysis
3.9 Data Standardization
3.10 Data Discretization
3.11 Operational Business Factors to Consider in Data Processing
3.12 Extended Reading: Preprocessing Unstructured Data
Chapter 4 Avoiding the Big Pitfalls of Operational Data Analysis and Mining
4.1 Cluster Analysis
4.2 Regression Analysis
4.3 Classification Analysis
4.4 Association Analysis
4.5 Anomaly Detection Analysis
4.6 Time Series Analysis
4.7 Path, Funnel, Attribution, and Heatmap Analysis
4.8 Other Advice on Data Analysis and Mining
4.9 Extended Reading: Analysis and Mining of Unstructured Data
Chapter 5 Data-Driven Member Operations
5.1 Overview of Data-Driven Member Operations
5.2 Key Metrics for Data-Driven Member Operations
5.3 Application Scenarios for Data-Driven Member Operations
5.4 Analytical Models for Data-Driven Member Operations
5.5 Tips for Data-Driven Member Operations Analysis
5.6 “Home Truths” About Data-Driven Member Operations Analysis
5.7 Case: RFM-Based User Value Analysis

FAQ

这本书需要什么基础？

需要具备 Python 基础语法能力，最好对 pandas、numpy 有初步了解。书中不系统教编程入门，重点放在数据获取、预处理、建模方法与运营场景的结合上；有基本的统计概念会更容易理解回归、聚类等章节。

这本书和《利用Python进行数据分析》有什么不同？

《利用Python进行数据分析》侧重 pandas 等工具的操作与数据处理技术；本书技术部分之外还有一半篇幅讲会员、商品、流量、内容四大运营主题，包含指标体系、分析模型与完整案例，更强调分析结论如何回到业务场景落地。

书中的案例数据和代码怎么获取？

本书配套提供案例数据与带中文注释的源代码供读者下载运行。具体下载方式与获取渠道请以所购版本的版权页、前言或出版社官方说明为准，不同印次可能有所调整。

第一版和第二版该怎么选？

第一版 2017 年出版，ISBN 为 9787111584605；第二版 2019 年出版，ISBN 为 9787111627760，将 Python 升级到较新版本，并补充优化了部分案例。若环境较新，建议优先选第二版；本页面信息对应第一版。

零基础转行做数据分析适合读吗？

不太建议直接上手。建议先补 Python 语法和 pandas 基础，再读本书的预处理与建模部分；否则容易卡在环境配置和代码调试上，浪费了书中真正有价值的业务视角内容。

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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.