Getting Started with R for Data Science [Video]

More Information
Learn
  • Write R code that can be executed outside RStudio
  • Get data from numerous sources such as files, databases, and even Twitter
  • Clean data before the analysis phase begins
  • Load libraries into RStudio for use within the analysis phase
  • Perform data cleaning on a dataset  
  • Create a codebook so that the data can be presented in a summary
  • Understand how to use visualization to understand data and tell a story
About

The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community.

This course will take you on a journey to become an efficient data science practitioner as you thoroughly understand the key concepts of R. Starting from the absolute basics, you will quickly be introduced to programming in R. You will see how to load data into R for analysis, and get a good understanding of how to write R scripts. We will delve into data types in R, and you'll gain the ability to read and write data to and from databases as well as files. You will also get to know how to perform basic analysis of the data. 

By the end of the course, you will know how data science can be applied in practical conditions.

Style and Approach

This is a hands-on introductory course to help you analyze, interpret, and optimize data in R. We cover a range of topics with a brief discussion, followed by a simple example of the implementation.

Features
  • Find out how to load, analyze, and manipulate data from large datasets to make informed decisions
  • Be introduced to RStudio, R programming, as well as the tools available to mine data from social media
  • A tutorial with hands-on working examples of R programming 
Course Length 1 hours 39 minute
ISBN 9781785884252
Date Of Publication 29 Sep 2016

Authors

Richard Skeggs

Richard Skeggs is not new to big data as he has over 15 years of experience in creating big data repositories and solutions for large multinational organizations in Europe. Having become a single father, he has changed his focus and is now working within the academic and research community. Richard has special interest in big data and is currently undertaking research within the field. His research interests revolve around machine learning, data retrieval, and complex systems.

Mykola Kolisnyk

Mykola Kolisnyk has been working in test automation since 2004. He has been involved with various activities including creating test automation solutions from scratch, leading test automation teams, and working as a consultant with test automation processes. During his working career, he has had experience with different test automation tools such as Mercury WinRunner, MicroFocus SilkTest, SmartBear TestComplete, Selenium-RC, WebDriver, Appium, SoapUI, BDD frameworks, and many other different engines and solutions. He has had experience with multiple programming technologies based on Java, C#, Ruby, and so on, and with different domain areas such as healthcare, mobile, telecoms, social networking, business process modelling, performance and talent management, multimedia, e-commerce, and investment banking.