Introduction to data science : data analysis and prediction algorithms with R / Rafael A. Irizarry.

By: Irizarry, Rafael A [author.]
Publisher: [Boca Raton] : [CRC Press], [2019]Description: pages cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780367357986Subject(s): R (Computer program language) | Information visualization | Data mining | Statistics -- Data processing | Probabilities -- Data processing | Computer algorithms | Quantitative researchAdditional physical formats: Online version:: Introduction to data science.DDC classification: Grad. 005.362 LOC classification: QA276.45.R3 | I75 2019
Contents:
Installing R and RStudio -- Getting started with R and RStudio -- R Basics -- Programming basics -- The tidyverse -- Importing data -- Introduction to data visualization -- ggplot2 -- Visualizing data distributions -- Data visualization in practice -- Data visualization principles -- Robust summaries -- Introduction to statistics with R -- Probability -- Random variables -- Statistical inference -- Statistical models -- Regression -- Linear models -- Association is not causation -- Introduction to data wrangling -- Reshaping data -- Joining tables -- Web scraping -- String processing -- Parsing dates and times -- Text mining -- Introduction to machine learning -- Smoothing -- Cross validation -- The caret package -- Examples of algorithms -- Machine learning in practice -- Large datasets -- Clustering -- Introduction to productivty tools -- Accessing the terminal and installing Git -- Organizing with Unix -- Git and GitHub -- Reproducible projects with RStudio and R markdown.
Summary: "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"-- Provided by publisher.
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Item type Current location Home library Collection Call number Vol info Copy number Status Date due Barcode Item holds
Book COLLEGE LIBRARY
COLLEGE LIBRARY
RESERVE 		REFERENCE Grad. 005.362 I689 2020 c.2 (Browse shelf) 12346 2 Available 12346
Book GRADUATE LIBRARY
GRADUATE LIBRARY
Graduate School 		NON-FICTION Grad. 005.362 I689 2020 (Browse shelf) 9889 Available 9889
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Installing R and RStudio -- Getting started with R and RStudio -- R Basics -- Programming basics -- The tidyverse -- Importing data -- Introduction to data visualization -- ggplot2 -- Visualizing data distributions -- Data visualization in practice -- Data visualization principles -- Robust summaries -- Introduction to statistics with R -- Probability -- Random variables -- Statistical inference -- Statistical models -- Regression -- Linear models -- Association is not causation -- Introduction to data wrangling -- Reshaping data -- Joining tables -- Web scraping -- String processing -- Parsing dates and times -- Text mining -- Introduction to machine learning -- Smoothing -- Cross validation -- The caret package -- Examples of algorithms -- Machine learning in practice -- Large datasets -- Clustering -- Introduction to productivty tools -- Accessing the terminal and installing Git -- Organizing with Unix -- Git and GitHub -- Reproducible projects with RStudio 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"-- Provided by publisher.

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