Best books for exploratory data analysis

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best books for exploratory data analysis

Exploratory Data Analysis Using R - CRC Press Book

Skip to main content. Exploratory Data Analysis. Only 13 left in stock more on the way. In the preface, Tukey writes, "this book This book has served me well for decades: I have used most of its techniques in my statistical consulting practice and, more recently, have used it as a foundation for courses in data analysis that range from a few hours to an entire semester.
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Python Data Science Handbook Jake VanderPlas: Review

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R for Data Science by Garrett Grolemund, Hadley Wickham

This chapter will show you how to use visualization and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle. EDA is not a formal process with a strict set of rules. More than anything, EDA is a state of mind. During the initial phases of EDA you should feel free to investigate every idea that occurs to you. Some of these ideas will pan out, and some will be dead ends.

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By using our site, you acknowledge that you have read and understand our Cookie Policy , Privacy Policy , and our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. I've been reading Tukey's book "Exploratory Data Analysis". Is there a more 'modern' successor which takes into account that we can now instantaneosly plot large data sets? The closest thing is Cleveland's Visualizing Data.

This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. Thanks for purchasing this book. For those of you who purchased a printed copy of this book, I encourage you to go to the Leanpub web site and obtain the e-book version , which is available for free.


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