Exploratory Data Analysis with MATLAB

Praise for the Second Edition:
The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB.--Adolfo Alvarez Pinto, International Statistical Review

Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA.

--David A Huckaby, MAA Reviews

Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models.

Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book's website.

New to the Third Edition

• Random projections and estimating local intrinsic dimensionality
• Deep learning autoencoders and stochastic neighbor embedding
• Minimum spanning tree and additional cluster validity indices
• Kernel density estimation
• Plots for visualizing data distributions, such as beanplots and violin plots
• A chapter on visualizing categorical data

第二版的好評如潮：
作者們呈現了一本直觀且易讀的書籍... 附有許多例子、練習題、良好的參考資料和全面的附錄，適合對MATLAB不熟悉的讀者。 - Adolfo Alvarez Pinto，《國際統計評論》

使用MATLAB進行EDA的從業人員將會想要一本這樣的書籍... 作者們通過整合許多EDA例程做出了巨大貢獻，但他們在這本充滿活力的書中的主要成就是提供了理解和執行EDA所需的工具和知識。 - David A Huckaby，《MAA評論》

探索性數據分析（EDA）是數據分析過程中的重要部分。本書介紹的方法應該成為每個數據科學家的工具包中的一部分。隨著計算能力的提高和數據集的大小和複雜性的增加，EDA在生成假設和模型之前對於可視化和總結數據的重要性更加突出。

《MATLAB探索性數據分析，第三版》從計算的角度介紹了EDA方法，並使用了許多例子和應用來展示這些方法在實踐中的應用。作者使用MATLAB代碼、偽代碼和算法描述來說明概念。書中的例子、數據集和EDA工具箱的MATLAB代碼可在書籍網站上下載。

第三版的新增內容包括：
- 隨機投影和估計局部內在維度
- 深度學習自編碼器和隨機鄰居嵌入
- 最小生成樹和其他集群有效性指標
- 核密度估計
- 用於可視化數據分佈的圖表，例如豆圖和小提琴圖
- 一章關於可視化分類數據的內容

Wendy L. Martinez is a mathematical statistician with the U.S. Bureau of Labor Statistics. She is a fellow of the American Statistical Association, a co-author of several popular Chapman & Hall/CRC books, and a MATLAB(R) user for more than 20 years. Her research interests include text data mining, probability density estimation, signal processing, scientific visualization, and statistical pattern recognition. She earned an M.S. in aerospace engineering from George Washington University and a Ph.D. in computational sciences and informatics from George Mason University.

Angel R. Martinez is fully retired after a long career with the U.S. federal government and as an adjunct professor at Strayer University, where he taught undergraduate and graduate courses in statistics and mathematics. Before retiring from government service, he worked for the U.S. Navy as an operations research analyst and a computer scientist. He earned an M.S. in systems engineering from the Virginia Polytechnic Institute and State University and a Ph.D. in computational sciences and informatics from George Mason University.

Since 1984, Jeffrey L. Solka has been working in statistical pattern recognition for the Department of the Navy. He has published over 120 journal, conference, and technical papers; has won numerous awards; and holds 4 patents. He earned an M.S. in mathematics from James Madison University, an M.S. in physics from Virginia Polytechnic Institute and State University, and a Ph.D. in computational sciences and informatics from George Mason University.

Wendy L. Martinez是美國勞工統計局的數理統計學家。她是美國統計學會的會士，也是幾本受歡迎的Chapman & Hall/CRC書籍的合著者，並且使用MATLAB(R)超過20年。她的研究興趣包括文本數據挖掘、概率密度估計、信號處理、科學可視化和統計模式識別。她在喬治華盛頓大學獲得航空航天工程碩士學位，並在喬治梅森大學獲得計算科學和信息學博士學位。

Angel R. Martinez在美國聯邦政府擔任長期職業生涯後已全面退休，並在Strayer大學擔任兼職教授，教授統計學和數學的本科和研究生課程。在從政府服務退休之前，他曾在美國海軍擔任運營研究分析師和計算機科學家。他在維吉尼亞理工大學獲得系統工程碩士學位，並在喬治梅森大學獲得計算科學和信息學博士學位。

自1984年以來，Jeffrey L. Solka一直在海軍部門從事統計模式識別工作。他發表了120多篇期刊、會議和技術論文，獲得了眾多獎項，並擁有4項專利。他在詹姆斯麥迪遜大學獲得數學碩士學位，在維吉尼亞理工大學獲得物理學碩士學位，並在喬治梅森大學獲得計算科學和信息學博士學位。