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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation.

This book is a textbook for a first cours

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Book ID Asin: B081D99D9H
Book Title: Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series)
Book Author: Rafael A. Irizarry
Book Format: unknownBook Price: unknown
Book Category: Kindle Store, Kindle eBooks, Science & Math and unknown
Book Rating: 29 ratings

Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series) by Rafael A. Irizarry Book Review

Name: I I
Rating: 3.0 out of 5 stars
Title: Great book but book, five stars, but book damaged
Date: Reviewed in the United States on February 7, 2021
Review: The book without any doubt is five star. Mr. Irizarry book is an exceptional teaching tool for the subject, and the examples are outstanding. Thank you Rafael!!
In relation to the shipment, the book was damaged as seen in the picture.

Name: Kirk Demlinger
Rating: 5.0 out of 5 stars
Title: Detailed and Comprehensive Guide to Using R for Data Science.
Date: Reviewed in the United States on July 26, 2021
Review: Very detailed, step-by-step guide to using R for data science. Certainly the most comprehensive text I've seen.

Name: BoldlyAsserted
Rating: 2.0 out of 5 stars
Title: Not a good Introduction to data science
Date: Reviewed in the United States on June 5, 2020
Review: As the other review remarked. If you've been using R daily for 4 years, this might be the book for you. If your are trying to learn R, this is not the book for you.

This book is mediocre in it presentation of R and poor in it teaching of probability and statistic. It takes a special gift to teach those topics. They are exceptionally confusing to learn because of the plethora of seemingly random formulas. There are a good textbooks on this, but sadly, this is not one of them. I read the book online as part of a course and quite frankly, I'm surprised it was able to get published.

The explanations are not particularly clear, the examples are glossed over and the organizations is a bit random.. The author is probably a whiz at stats, but that is a whole different set of skills to be a teacher of R, stats, and data science.

To learn R, Hadley Wickham and Garrett Grolemund are simply superb. Excellent communicators with a clear pedagogical strategy. R for Data Science and Hand on Programming with R are truly excellent books.

Name: Frederic Paik Schoenberg
Rating: 5.0 out of 5 stars
Title: Excellent textbook for statistical computing
Date: Reviewed in the United States on September 29, 2020
Review: This is a superbly written book that emphasizes all the most important topics in modern computing with R. It tackles the most important problems, explains things clearly and thoroughly, and trains the reader to use the most modern, state-of-the-art R programming techniques. Rather than getting bogged down in details of technical grammar issues and nuts and bolts of how certain computational algorithms are written, this book is more of a how to, teaching the reader how to use R packages and functions to do their own statistical computing.