Book Details:
Pages: | 400 |
Published: | Sep 26 2013 |
Posted: | Nov 19 2014 |
Language: | English |
Book format: | PDF |
Book size: | 13.55 MB |
Book Description:
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you'll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you've learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code
Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications Overview Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of Ra cross-platform, zero-cost statistical programming envi...
Develop key skills and techniques with R to create and customize data mining algorithms About This BookDevelop a sound strategy for solving predictive modeling problems using the most popular data mining algorithmsGain understanding of the major methods of predictive modelingPacked with practical advice and tips to help you get to grips with data miningWho This Book Is ForThis book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining ...
Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data About This Book * Unlock and discover how to tackle clusters of raw data through practical examples in R * Explore your data and create your own models from scratch * Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide Who This Book Is For This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement. What You Will ...
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