Beginning data science in R 4 : data analysis, visualization, and modelling for the data scientist
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Format
Edition
2nd ed..
Language
English
ISBN
9781484281550, 1484281551
UPC
10.1007/978-1-4842-8155-0
Notes
General Note
Includes 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. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition 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. Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well.
Local note
O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Mailund, T. (2022). Beginning data science in R 4: data analysis, visualization, and modelling for the data scientist (2nd ed..). Apress.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Mailund, Thomas. 2022. Beginning Data Science in R 4: Data Analysis, Visualization, and Modelling for the Data Scientist. New York, New York: Apress.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Mailund, Thomas. Beginning Data Science in R 4: Data Analysis, Visualization, and Modelling for the Data Scientist New York, New York: Apress, 2022.
Harvard Citation (style guide)Mailund, T. (2022). Beginning data science in R 4: data analysis, visualization, and modelling for the data scientist. 2nd ed.. New York, New York: Apress.
MLA Citation, 9th Edition (style guide)Mailund, Thomas. Beginning Data Science in R 4: Data Analysis, Visualization, and Modelling for the Data Scientist 2nd ed.., Apress, 2022.
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
de23c9ac-1e4f-f3d0-eb80-93888bf7aa43-eng
Grouping Information
| Grouped Work ID | de23c9ac-1e4f-f3d0-eb80-93888bf7aa43-eng |
|---|---|
| Full title | beginning data science in r 4 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 03:32:01AM |
Book Cover Information
| Image Source | contentCafe |
|---|---|
| First Loaded | Aug 5, 2023 |
| Last Used | Nov 2, 2024 |
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| 505 | 0 | |a 1: Introduction -- 2: Introduction to R Programming -- 3: Reproducible Analysis -- 4: Data Manipulation -- 5: Visualizing Data -- 6: Working with Large Data Sets -- 7: Supervised Learning -- 8: Unsupervised Learning -- 9: Project 1: Hitting the Bottle -- 10: Deeper into R Programming -- 11: Working with Vectors and Lists -- 12: Functional Programming -- 13: Object-Oriented Programming -- 14: Building an R Package -- 15: Testing and Package Checking -- 16: Version Control -- 17: Profiling and Optimizing -- 18: Project 2: Bayesian Linear Progression -- 19: Conclusions. | |
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