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Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series) - Hardcover
Synopsis
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.
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About the Author
Rafael A. Irizarry is professor of data sciences at the Dana-Farber Cancer Institute, professor of biostatistics at Harvard, and a fellow of the American Statistical Association. Dr. Irizarry is an applied statistician and during the last 20 years has worked in diverse areas, including genomics, sound engineering, and public health. He disseminates solutions to data analysis challenges as open source software, tools that are widely downloaded and used. Prof. Irizarry has also developed and taught several data science courses at Harvard as well as popular online courses.
Review
"I think the book would be perfect for schools looking to make a transition to a model where introduction to data science takes the place of introduction to statistics and maybe introductory computer science." ~Arend Kuyper, Northwestern University
"About this title" may belong to another edition of this title.
- PublisherChapman and Hall/CRC
- Publication date2019
- ISBN 10 0367357984
- ISBN 13 9780367357986
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages713
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series)
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R (Chapman & Hall/CRC Data Science Series)
Published by
Chapman and Hall/CRC, 2019
ISBN 10: 0367357984
ISBN 13: 9780367357986
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Introduction to Data Science (Hardcover)
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Hardcover. Condition: new. Hardcover. Covers the basics of R and the tidyverse Demonstrate how to use ggplot2 to generate graphs and describe important Data Visualization principlesIntroduces Data Wranglin topics such as web scrapping, using regular expressions, and joining and reshaping data tables using the tidyverse tools Illustrates the importance of statistics in data analysis using case studies Uses the caret package to build prediction algorithms including K-nearest Neighbors and Random ForestsIncludes tools used on a day-to-day basis in data science projects including RStudio, UNIX/Linux shell, Git and GitHub, and knitr and R Markdown The book begins by going over the basics of R and the tidyverse. You learn R throughout the book, but in the first part we go over the building blocks needed to keep learning during the rest of the book. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780367357986
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