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Beginning data science in R 4 : data analysis, visualization, and modelling for the data scientist / Thomas Mailund.

Mailund, Thomas, author.
2022
QA276.45.R3
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Title
Beginning data science in R 4 : data analysis, visualization, and modelling for the data scientist / Thomas Mailund.
Author
Mailund, Thomas, author.
Edition
2nd ed..
ISBN
9781484281550 (electronic bk.)
1484281551 (electronic bk.)
9781484281543
1484281543
DOI
https://doi.org/10.1007/978-1-4842-8155-0
Published
New York, New York : Apress, [2022]
Language
English
Description
1 online resource (528 pages) : illustrations
Item Number
10.1007/978-1-4842-8155-0 doi
Call Number
QA276.45.R3
Dewey Decimal Classification
519.50285/536
Summary
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.
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Includes index.
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Access limited to authorized users.
Available in Other Form
Print version: 9781484281543
Beginning data science in R 4: data analysis, visualization, and modelling for the data scientist.
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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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