# Beginning data science in R: data analysis, visualization, and modelling for the data scientist

##### By: Mailund, Thomas

Material type: TextPublisher: New York Apress Media 2018Description: xxvii, 352p. With indexISBN: 9781484240588Subject(s): R (computer program language) | Quantitative research | Computer software -- development | Computer -- programming languagesDDC classification: 001.42 Summary: 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. 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 https://www.apress.com/in/book/9781484226704Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|

Books | Vikram Sarabhai Library General Stacks | Slot 3 (0 Floor, West Wing) | Non-fiction | 001.42 M2B3 (Browse shelf) | Checked out | 13/03/2021 | 198793 |

Table of contents (14 chapters)

Chapter 1: Introduction to R Programming

Chapter 2: Reproducible Analysis

Chapter 3: Data Manipulation

Chapter 4: Visualizing Data

Chapter 5: Working with Large Datasets

Chapter 6: Supervised Learning

Chapter 7: Unsupervised Learning

Chapter 8: More R Programming

Chapter 9: Advanced R Programming

Chapter 10: Object Oriented Programming

Chapter 11: Building an R Package

Chapter 12: Testing and Package Checking

Chapter 13: Version Control

Chapter 14: Profiling and Optimizing

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.

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

https://www.apress.com/in/book/9781484226704

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