Data Analysis and Mining Algorithms: Python in Practice

Name: Data Analysis and Mining Algorithms: Python in Practice
ISBN: 9787121422003

张晓东Publishing House of Electronics Industry · ChinaNov 2021

3.8

Word count: 60K words
Chapters: 42
ISBN: 9787121422003

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Summary

This book is a learning guide to algorithms related to data analysis, mainly covering concepts of data analysis and data mining, data thinking, and the principles and implementation methods of various data analysis algorithms. For each data analysis algorithm, the book introduces the mathematical principles, Python code implementation, and practical cases, with rich content that is easy to understand. The book has 9 chapters. Chapter 1 introduces the relationship between data mining, data analysis, and machine learning; Chapter 2 introduces the data thinking that data analysts should possess, including data thinking cognition and data mining "laws"; Chapters 3–9 introduce the principles, implementation methods, and practical cases of various data analysis algorithms, including logistic regression, decision trees, naive Bayes, cluster analysis, association rules, artificial neural networks, and ensemble learning. This book is suitable for readers engaged in data analysis for self-study, and can also serve as a reference for product managers, operations staff, marketing staff, and readers interested in data analysis.

Editor's review

Many data analysis books either cover only tool operations or pile up mathematical formulas; this book tries to bridge the two. For each algorithm it first explains the mathematical principles, then gives a Python implementation, and finally lands on a practical case, forming a loop of "understand the principle—know how to implement—be able to apply." Chapter 2 is devoted to data thinking, reminding readers that algorithms are only means and that clarifying the business problem first is key—a point rare in similar books. Logistic regression, decision trees, naive Bayes, clustering, association rules, neural networks, and ensemble learning cover the main algorithms from beginner to intermediate level, with a clear structure suitable for progressing chapter by chapter. Note that the book is positioned as a learning guide rather than a tool manual: code examples serve algorithm understanding and do not pursue engineering or tuning details. It is suitable as a course companion or self-study mainline, and works best when you code along while reading.

Who it's for

A good fit for

College students who want a systematic introduction to data analysis algorithms and have basic Python syntax
Product managers who need to understand algorithm principles to collaborate with data teams
Operations and business analysts who want to move from Excel or SQL to Python modeling
Job seekers preparing for interviews who need to review common algorithm principles and implementation ideas

Not a good fit for

Complete beginners with no programming background who do not yet understand variables and loops
Technical staff seeking big data platform architecture, distributed computing, and engineering deployment solutions
Readers who only want to quickly apply ready-made models and are unwilling to understand mathematical principles

Reading tips

First read Chapters 1 and 2 to build an overall framework of data mining and data thinking, then move on to the algorithm chapters
Proceed through "principle—code—case" in each chapter, running and modifying the example code yourself
Prepare a small dataset of your own and reproduce the full workflow each time you finish an algorithm
After reading, use a table to compare each algorithm's assumptions, applicable scenarios, pros, and cons for review

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

版权信息
内容简介
前言
第1章数据分析概述
1.1 什么是数据挖掘
1.2 数据挖掘与数据分析的关系
1.3 数据挖掘与机器学习的关系
1.4 机器学习算法简介
第2章数据思维
2.1 数据思维认知
2.2 数据思维认知的主观性与客观性
2.3 数据挖掘“定律”
第3章逻辑回归
3.1 逻辑回归基础：从线性回归到逻辑回归
3.2 逻辑回归函数构建
3.3 逻辑回归问题求解
3.4 逻辑回归模型评估
3.5 Python代码实现
第4章决策树
4.1 决策树基础
4.2 决策树算法
4.3 Python代码实现
第5章朴素贝叶斯
5.1 概率论基础
5.2 从贝叶斯公式到朴素贝叶斯分类
5.3 Python代码实现
第6章聚类分析
6.1 聚类分析基础
6.2 聚类算法
6.3 Python代码实现
第7章关联规则
7.1 关联规则基础
7.2 关联规则算法
7.3 Python代码实现
第8章人工神经网络
8.1 人工神经网络基础
8.2 BP（误差逆传播前馈）神经网络
8.3 Python代码实现
第9章集成学习
9.1 集成学习基础
9.2 集成学习算法
参考资料

FAQ

Can I read this book directly without a machine learning foundation?

Yes, but you need basic Python syntax. The book starts with the relationship between data mining, data analysis, and machine learning before moving to specific algorithms, so the entry threshold is set relatively gently. It is recommended to read Chapters 1 and 2 first; you do not need to fully grasp the mathematical derivations at once—first understand what problem the algorithm solves, then return to fill in the formulas.

What kind of runtime environment do the cases in the book require?

The algorithms are implemented in Python, so readers need to install Python and common data analysis libraries such as NumPy, pandas, and scikit-learn. It is recommended to run the code in a local or notebook environment while reading and try changing parameters to observe how results change; this is more effective for understanding algorithm behavior than just reading code.

Is this book more theoretical or more practical?

It balances both, but the focus is on helping readers understand algorithms. Each algorithm includes mathematical principles, Python code implementation, and practical cases. It is suitable as a learning mainline to build algorithm understanding; for engineering implementation, performance tuning, and deployment, you will need supplementary engineering materials.

What can product managers gain from reading this book?

They can build basic judgment about common algorithms: what problems suit classification, what problems suit clustering, and roughly how to interpret model results. The data thinking content in Chapter 2 is especially suitable for non-technical roles, helping them propose requirements more accurately, evaluate solutions, and reduce misunderstandings when communicating with data teams.

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About the author

张晓东 Author

Zhang Xiaodong: publicly available information is limited, and no clear record of his identity or career has been found. Yet the nine works held in the collection sketch the outline of a cross-disciplinary writer. His interests span the traditional and the modern: on one hand, he examines ancient Chinese literature on the pipa and ruanxian, sorting through historical sources on these instruments; on the other, he writes introductory books on electronic components and small maker projects, turning energy conservation, environmental protection, and playful creativity into hands-on practical projects. He has also touched on patent searching and information analysis, Python data analysis and mining algorithms, craft-art tutorials, and a beginner's guide to Chinese medicine. This range—from classical textual research to industrial technology, computing, and popular science—reveals a writing approach aimed at beginners and focused on practicality and operability, with works mostly serving teaching and self-study.