Beginning data science in R : data analysis, visualization, and modelling for the data scientist
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Language
English
ISBN
9781484226711, 1484226712
UPC
10.1007/978-1-4842-2671-1, 10.1007/978-1-4842-2
Notes
Bibliography
Includes bibliographical references and index.
Description
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. You will: Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code.
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O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Mailund, T. (2017). Beginning data science in R: data analysis, visualization, and modelling for the data scientist . Apress.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Mailund, Thomas. 2017. Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist. [New York, NY]: Apress.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Mailund, Thomas. Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist [New York, NY]: Apress, 2017.
Harvard Citation (style guide)Mailund, T. (2017). Beginning data science in R: data analysis, visualization, and modelling for the data scientist. [New York, NY]: Apress.
MLA Citation, 9th Edition (style guide)Mailund, Thomas. Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist Apress, 2017.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
d04a79a2-aeaa-57c2-a2a5-be5118e0bab6-eng
Grouping Information
Grouped Work ID | d04a79a2-aeaa-57c2-a2a5-be5118e0bab6-eng |
---|---|
Full title | beginning data science in r data analysis visualization and modelling for the data scientist |
Author | mailund thomas |
Grouping Category | book |
Last Update | 2025-01-24 12:33:29PM |
Last Indexed | 2025-02-07 03:30:05AM |
Book Cover Information
Image Source | contentCafe |
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First Loaded | Jul 8, 2023 |
Last Used | Nov 4, 2024 |
Marc Record
First Detected | Mar 14, 2023 08:06:06 AM |
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Last File Modification Time | Mar 14, 2023 08:06:06 AM |
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245 | 1 | 0 | |a Beginning data science in R :|b data analysis, visualization, and modelling for the data scientist /|c Thomas Mailund. |
264 | 1 | |a [New York, NY] :|b Apress,|c [2017] | |
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505 | 0 | |a Introduction to R programming -- Reproducible analysis -- Data manipulation -- Visualizing data -- Working with large datasets -- Supervised learning -- Unsupervised learning -- More R programming -- Advanced R programming -- Object oriented programming -- Building an R package -- Testing and package checking -- Version control -- Profiling and optimizing. | |
520 | |a Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. You will: Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code. | ||
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