Beginning data science in R 4 : data analysis, visualization, and modelling for the data scientist / Thomas Mailund.
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 softwar...
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Full Text (via O'Reilly/Safari) |
|---|---|
| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
New York, New York :
Apress,
[2022]
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| Edition: | 2nd ed.. |
| Subjects: |
MARC
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| 245 | 1 | 0 | |a Beginning data science in R 4 : |b data analysis, visualization, and modelling for the data scientist / |c Thomas Mailund. |
| 250 | |a 2nd ed.. | ||
| 264 | 1 | |a New York, New York : |b Apress, |c [2022] | |
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| 500 | |a Includes index. | ||
| 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. 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. | ||
| 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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