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Data Analysis with Open Source Tools

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Synopses & Reviews

Publisher Comments:

'

What people are saying about R in a Nutshell

\"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides \'real world\' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians.\"

--Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University

\"R in a Nutshell is an ideal book for getting started with R. Newcomers will find the fundamentals for performing statistical analysis and graphics, all illustrated with practical examples. This book is an invaluable reference for anyone who wants to learn what R is and what is can do, even for longtime R users looking for new tips and tricks.\"

--David M. Smith, Editor of the \"Revolutions\" blog at REvolution Computing

Why learn R? Because it\'s rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You\'ll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.

The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you\'ll learn how you can use this remarkable tool to solve your own data analysis problems.

  • Understand the basics of the language, including the nature of R objects
  • Learn how to write R functions and build your own packages
  • Work with data through visualization, statistical analysis, and other methods
  • Explore the wealth of packages contributed by the R community
  • Become familiar with the lattice graphics package for high-level data visualization
  • Learn about bioinformatics packages provided by Bioconductor
'

Book News Annotation:

Janert, a data and mathematical modeling consultant, presents this guide to the principles and practice of business data analysis. The work is divided into four sections beginning with a holistic look at what data sets have to show the observer when approached without preconceived criteria and proceeding through modeling, data mining, and the uses of business data such as business intelligence reporting, financial planning and predictive analytics. Chapters include formulas, graphs and code examples and a collection of appendices discuss specific data analysis software tools, useful calculus functions and information on collecting data sets. Access to a companion website that provides examples and further information is provided. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)

Synopsis:

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.

Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve — rather than rely on tools to think for you.

  • Use graphics to describe data with one, two, or dozens of variables
  • Develop conceptual models using back-of-the-envelope calculations, as well as scaling and probability arguments
  • Mine data with computationally intensive methods such as simulation and clustering
  • Make your conclusions understandable through reports, dashboards, and other metrics programs
  • Understand financial calculations, including the time-value of money
  • Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations
  • Become familiar with different open source programming environments for data analysis

"Finally, a concise reference for understanding how to conquer piles of data." --Austin King, Senior Web Developer, Mozilla

"An indispensable text for aspiring data scientists." --Michael E. Driscoll, CEO/Founder, Dataspora

Synopsis:

These days it seems like everyone is collecting data. But all of that data is just raw information — to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless.

In Real World Data Analysis, author Philipp Janert teaches you how to think about data: how to effectively approach data analysis problems, and how to extract all of the available information from your data. Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics. He also reveals how seat-of-the-pants knowledge can lead you to the best approach right from the start, and how to assess results to determine if they're meaningful.

About the Author

Philipp K. Janert is Chief Consultant at Principal Value, LLC. He has worked for small start-ups and in large corporate environments, both in the US and overseas, including several years at Amazon.com, where he initiated and led several projects to improve Amazon's order fulfillment processes. Philipp K. Janert has written about software and software development for the O'Reilly Network, IBM developerWorks, IEEE Software, and Linux Magazine. He holds a Ph.D. in Theoretical Physics from the University of Washington. Visit his website at www.principal-value.com.

Table of Contents

PrefaceR Basics
  • Chapter 1: Getting and Installing R
  • Chapter 2: The R User Interface
  • Chapter 3: A Short R Tutorial
  • Chapter 4: R Packages
The R Language
  • Chapter 5: An Overview of the R Language
  • Chapter 6: R Syntax
  • Chapter 7: R Objects
  • Chapter 8: Symbols and Environments
  • Chapter 9: Functions
  • Chapter 10: Object-Oriented Programming
  • Chapter 11: High-Performance R
Working with Data
  • Chapter 12: Saving, Loading, and Editing Data
  • Chapter 13: Preparing Data
  • Chapter 14: Graphics
  • Chapter 15: Lattice Graphics
Statistics with R
  • Chapter 16: Analyzing Data
  • Chapter 17: Probability Distributions
  • Chapter 18: Statistical Tests
  • Chapter 19: Power Tests
  • Chapter 20: Regression Models
  • Chapter 21: Classification Models
  • Chapter 22: Machine Learning
  • Chapter 23: Time Series Analysis
  • Chapter 24: Bioconductor
R ReferenceBibliographyColophon

Product Details

ISBN:
9780596802356
Author:
Janert, Philipp K
Publisher:
O'Reilly Media
Author:
Janert, Philipp K.
Author:
Adler, Joseph
Subject:
Mathematical & Statistical Software
Subject:
Mathematics -- Software.
Subject:
R;analysis;data;data mining;graphics;mathematics;matlab;modeling;programming;python;scipy;statistics;visualization
Copyright:
Edition Description:
Trade Paper
Publication Date:
20101131
Binding:
TRADE PAPER
Language:
English
Pages:
540
Dimensions:
9.19 x 7.00 in

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Related Subjects

» Computers and Internet » Database » Design
» Computers and Internet » Database » General
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Data Analysis with Open Source Tools New Trade Paper
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$39.99 In Stock
Product details 540 pages O'Reilly Media - English 9780596802356 Reviews:
"Synopsis" by ,

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.

Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve — rather than rely on tools to think for you.

  • Use graphics to describe data with one, two, or dozens of variables
  • Develop conceptual models using back-of-the-envelope calculations, as well as scaling and probability arguments
  • Mine data with computationally intensive methods such as simulation and clustering
  • Make your conclusions understandable through reports, dashboards, and other metrics programs
  • Understand financial calculations, including the time-value of money
  • Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations
  • Become familiar with different open source programming environments for data analysis

"Finally, a concise reference for understanding how to conquer piles of data." --Austin King, Senior Web Developer, Mozilla

"An indispensable text for aspiring data scientists." --Michael E. Driscoll, CEO/Founder, Dataspora

"Synopsis" by ,

These days it seems like everyone is collecting data. But all of that data is just raw information — to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless.

In Real World Data Analysis, author Philipp Janert teaches you how to think about data: how to effectively approach data analysis problems, and how to extract all of the available information from your data. Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics. He also reveals how seat-of-the-pants knowledge can lead you to the best approach right from the start, and how to assess results to determine if they're meaningful.

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