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[[Image:Exploratory-data-analysis-with-MATLAB.jpg|150px|]]

[[Image:Exploratory-data-analysis-with-MATLAB.jpg|150px|]]

<h3 style="text-decoration:none;">Exploratory data analysis with MATLAB, 3rd edition</h3>

<h3 style="text-decoration:none;">Exploratory data analysis with MATLAB, 3rd edition</h3>

<p class="author">by Wendy L Martinez, Angel R Martinez, and Jeffrey Solka</p>

<p class="author">Wendy L Martinez, Angel R Martinez, and Jeffrey Solka</p>

<p>(In English) 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;The authors put a computational emphasis on the methods used to visualise and summarise data before making model assumptions to generate hypotheses. They use MATLAB code and algorithmic descriptions to provide the user with state-of-the-art techniques for finding patterns and structure in data.</p>

<p>(In English) 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;The authors put a computational emphasis on the methods used to visualise and summarise data before making model assumptions to generate hypotheses. They use MATLAB code and algorithmic descriptions to provide the user with state-of-the-art techniques for finding patterns and structure in data.</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>