logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Introduction To Data Science Data Wrangling And Visualization With R 2nd Edition 2nd Rafael A Irizarry

  • SKU: BELL-197741106
Introduction To Data Science Data Wrangling And Visualization With R 2nd Edition 2nd Rafael A Irizarry
$ 31.00 $ 45.00 (-31%)

4.3

58 reviews

Introduction To Data Science Data Wrangling And Visualization With R 2nd Edition 2nd Rafael A Irizarry instant download after payment.

Publisher: Chapman and Hall/CRC
File Extension: PDF
File size: 192.46 MB
Pages: 346
Author: Rafael A. Irizarry
ISBN: 9781032116556, 1032116552
Language: English
Year: 2024
Edition: 2nd

Product desciption

Introduction To Data Science Data Wrangling And Visualization With R 2nd Edition 2nd Rafael A Irizarry by Rafael A. Irizarry 9781032116556, 1032116552 instant download after payment.

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 course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture.

The author uses motivating case studies that realistically mimic a data scientist's experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems.

The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

A complete solutions manual is available to registered instructors who require the text for a course.

Related Products