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.