Beginning Data Science in R 4

Data Analysis, Visualization, and Modelling for the Data Scientist

Thomas Mailund (Autor)

Buch | Softcover

511 Seiten

2022 | 2nd ed.
Apress (Verlag)
978-1-4842-8154-3 (ISBN)

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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.
Source code is available at github.com/Apress/beg-data-science-r4.

What You Will Learn

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

Who This Book Is For
Those with some data science or analytics background, but not necessarily experience with the R programming language.

Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. His background is in math and computer science but for the last decade his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.

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.

Erscheinungsdatum	27.06.2022
Zusatzinfo	100 Illustrations, black and white; XXVIII, 511 p. 100 illus.
Verlagsort	Berkley
Sprache	englisch
Maße	178 x 254 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
	Informatik ► Theorie / Studium ► Compilerbau
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	AI • Analytics • Big Data • Cloud • Coding • Data Science • Deep learning • machine learning • programming • R • Software • Statistics
ISBN-10	1-4842-8154-3 / 1484281543
ISBN-13	978-1-4842-8154-3 / 9781484281543
Zustand	Neuware