R Programming for Data Science

Covers the fundamentals of R programming, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization.

**Tag(s):**
Big Data
Data Science
R
Statistics

**Publication date**: 03 Aug 2016

**ISBN-10**:
n/a

**ISBN-13**:
n/a

**Paperback**:
182 pages

**Views**: 7,078

R Programming for Data Science

Covers the fundamentals of R programming, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization.

Note:

This book is available at Leanpub. You can download this book for free, or you can choose to pay (the suggested price at this time of writing is USD 20.00).

From the Description:

From the Preface:

This book is available at Leanpub. You can download this book for free, or you can choose to pay (the suggested price at this time of writing is USD 20.00).

From the Description:

Roger D. Peng wrote:This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science. See the packages below to obtain datasets, R code files, and video lectures. Printed copies of this book are available through Lulu.

From the Preface:

Roger D. Peng wrote:This book comes from my experience teaching R in a variety of settings and through different stages of its (and my) development. Much of the material has been taken from by Statistical Computing class as well as the R Programming class I teach through Coursera.

I'm looking forward to teaching R to people as long as people will let me, and I'm interested in seeing how the next generation of students will approach it (and how my approach to them will change). Overall, it's been just an amazing experience to see the widespread adoption of R over the past decade. I'm sure the next decade will be just as amazing.

Tweet

About The Author(s)

Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is also a co-founder of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the general public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui.

Book Categories

Computer Science
Introduction to Computer Science
Introduction to Computer Programming
Algorithms and Data Structures
Artificial Intelligence
Computer Vision
Machine Learning
Neural Networks
Game Development and Multimedia
Data Communication and Networks
Coding Theory
Computer Security
Information Security
Cryptography
Information Theory
Computer Organization and Architecture
Operating Systems
Image Processing
Parallel Computing
Concurrent Programming
Relational Database
Document-oriented Database
Data Mining
Big Data
Data Science
Digital Libraries
Compiler Design and Construction
Functional Programming
Logic Programming
Object Oriented Programming
Formal Methods
Software Engineering
Agile Software Development
Information Systems
Geographic Information System (GIS)

Mathematics
Mathematics
Algebra
Abstract Algebra
Linear Algebra
Number Theory
Numerical Methods
Precalculus
Calculus
Differential Equations
Category Theory
Proofs
Discrete Mathematics
Theory of Computation
Graph Theory
Real Analysis
Complex Analysis
Probability
Statistics
Game Theory
Queueing Theory
Operations Research
Computer Aided Mathematics

Supporting Fields
Web Design and Development
Mobile App Design and Development
System Administration
Cloud Computing
Electric Circuits
Embedded System
Signal Processing
Integration and Automation
Network Science
Project Management

Operating System
Programming/Scripting
Ada
Assembly
C / C++
Common Lisp
Forth
Java
JavaScript
Lua
Microsoft .NET
Rexx
Perl
PHP
Python
R
Rebol
Ruby
Scheme
Tcl/Tk

Miscellaneous
Sponsors