Beginning Data Science in R

Thomas Mailund (Autor)

Buch | Softcover

352 Seiten

2017
Apress (Verlag)
978-1-4842-2670-4 (ISBN)

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Beginning Data Science in R 4

Thomas Mailund

2022

Buch | Softcover

CHF 82, ³⁵

zur Neuauflage

Gives you everything you need to know to get started in data science and R programming
A unique book by a data science expert
Based on a successful lecture series

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.

Beginning 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 learn how to

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

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 to R programming. 2. Reproducible analysis. 3. Data manipulation. 4. Visualizing and exploring data. 5. Working with large data sets.6. Supervised learning. 7. Unsupervised learning. 8. More R programming.9. Advanced R programming.10. Object oriented programming.11. Building an R package.12. Testing and checking. 13. Version control. 14. Profiling and optimizing.

Erscheinungsdatum	22.03.2017
Zusatzinfo	100 black & white illustrations, biography
Verlagsort	Berkley
Sprache	englisch
Maße	178 x 254 mm
Gewicht	728 g
Einbandart	kartoniert
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Mathematik / Informatik ► Informatik ► Netzwerke
	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
	Informatik ► Theorie / Studium ► Compilerbau
ISBN-10	1-4842-2670-4 / 1484226704
ISBN-13	978-1-4842-2670-4 / 9781484226704
Zustand	Neuware