Exploratory Data Analysis with MATLAB, Third Edition

by Wendy L. Martinez

Praise for the Second "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 ReviewsExploratory 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 EditionRandom projections and estimating local intrinsic dimensionalityDeep learning autoencoders and stochastic neighbor embeddingMinimum spanning tree and additional cluster validity indicesKernel density estimationPlots for visualizing data distributions, such as beanplots and violin plotsA chapter on visualizing categorical data

590 pages, Kindle Edition

Published August 7, 2017

Reviews

[Jin Shusong · Rating 4 out of 5]

This book thoroughly introduced the methods used in EDA. The authors explained the theory briefly and gave the algorithms in detail. The example codes were helpful. However, I found that the author failed to explain what we could find from the results of these methods. For example, the book introduced the intrinsic dimension. The authors also gave examples. But, what could I find and how can I use these results?

[Darin]

This book discusses ways to look at data to understand the underlying structure, including quite a bit about clustering. The included Matlab code is very helpful. While this isn't really a theory book, each chapter does have a bibliography that I found useful.