{"product_id":"exploratory-data-analysis-with-matlab-1","title":"Exploratory Data Analysis with MATLAB","description":"\u003cp\u003ePraise for the Second Edition:\u003cbr\u003e\u003ci\u003e\"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.\"\u003c\/i\u003e—Adolfo Alvarez Pinto, \u003cem\u003eInternational Statistical Review\u003c\/em\u003e \u003c\/p\u003e\u003cp\u003e\"\u003ci\u003ePractitioners 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.\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e—David A Huckaby, \u003cem\u003eMAA Reviews\u003c\/em\u003e\u003c\/p\u003e\u003cp\u003eExploratory 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. \u003c\/p\u003e\u003cp\u003eExploratory 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.\u003c\/p\u003e\u003cp\u003eNew to the Third Edition\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eRandom projections and estimating local intrinsic dimensionality\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eDeep learning autoencoders and stochastic neighbor embedding\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eMinimum spanning tree and additional cluster validity indices\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eKernel density estimation\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003ePlots for visualizing data distributions, such as beanplots and violin plots\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e \u003c\/p\u003e \u003c\/li\u003e\n\u003cli\u003eA chapter on visualizing categorical data\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":54224929194328,"sku":"9781032179056","price":64.99,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0278\/1295\/4195\/files\/9781032179056.jpg?v=1777090573","url":"https:\/\/agendabookshop.com\/products\/exploratory-data-analysis-with-matlab-1","provider":"Agenda Bookshop","version":"1.0","type":"link"}