Getting Started with R for Data Science
3.2 (3 ratings)
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Getting Started with R for Data Science

Unleash the powerful capabilities of R to work effectively with data
3.2 (3 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
18 students enrolled
Created by Packt Publishing
Last updated 11/2016
English
Current price: $10 Original price: $90 Discount: 89% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 1.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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
View Curriculum
Requirements
  • 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.
  • There is no need for in-depth knowledge of statistics, maths, or even programming.
Description

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.

About the Author

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

He worked as a permanent employee at ISD, GlobalLogic, Luxoft as well as has experience in freelancing activities and was invited as an independent consultant to introduce test automation approaches and practices to external companies.

He currently works as a mobile QA developer at Trainline.com Ltd.

He's one of the authors (together with Gennadiy Alpaev) of online SilkTest Manual and participated in the creation of TestComplete tutorial, both of which are the biggest related documentations available in RU-net..

Also, he participated as the reviewer of the following books:

  • TestComplete Cookbook (ISBN: 978-1-84969-358-5)
  • Spring Batch Essentials published by Packt Publishing (ISBN 139781783553372)
  • Mastering Data Analysis with R (ISBN 13: 9781783982028)

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.


Who is the target audience?
  • This course is for anyone, whether they are a hobbyist or professional data scientist.
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Curriculum For This Course
26 Lectures
01:38:54
+
Introducing R
5 Lectures 21:09

This will be an overview of entire course.

Preview 04:14

The aim of this video is to introduce the R programming language.

What is R?
02:34

The aim of this video is to start with some important basics such as function and variable declaration.

The Structure of the Language
03:52

The aim of this video is to introduce the different types of data structures available.

Data Structures within R
05:57

The aim of this video is to show that R scripts can be run from outside of the R IDE.

Writing a Simple Program in R
04:32
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Getting Data into R
4 Lectures 18:12

The aim of this video is to introduce the concept of a data frame. How it can be initialized as well as how data can be added to it.

Preview 05:34

The aim of this video is to introduce the concept of creating a data frame from a CSV file.

Creating a DataFrame from a CSV File
02:41

The aim of this video is to introduce the concept of ingesting data from a compressed file into R.

Creating a DataFrame from a Zip File
03:03

The aim of this video is to introduce the concept of ingesting data from a database into an R data frame. Introducing the tools required and the best practices in employing the tools.

Creating a DataFrame from a Database
06:54
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Cleaning and Blending Data
4 Lectures 18:26

The aim of this video is to allow the user to start cleaning datasets.

Preview 06:50

The aim of this video is to understand the process available to deal with missing values from a dataset.

Dealing with Null Values
04:03

The aim of this video is to look at the date format and process time.

Standardizing Date Formats
03:12

The aim of this video is to introduce the concept of a data frame, how it can be initialized, as well as how data can be added to it.

Blending Multiple DataFrames
04:21
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Cooking the Codebook
3 Lectures 09:50

The aim of this video is to allow the user to understand the importance of a codebook.

Preview 03:50

This video looks at creating the codebook from standard r functionality.

Creating the Codebook Using Standard R API Functionality
02:28

The aim of this video is to create a codebook using standard R functionality.

Manually Creating a Custom Codebook
03:32
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Data Mining and Analysis
5 Lectures 17:03

The aim of this video is to allow the user to understand what data mining is and the steps involved.

Preview 03:48

The aim of this video is to begin the process of creating the data story

The Tools and Techniques for Creating the Story
03:32

This video looks at creating a linear regression model.

Regression Analysis with R
02:23

The aim of this video is to look at clustering of data.

Clustering Data with R
03:20

The aim of this video is to introduce the concept of classifying data within R.

Classifying Data with R
04:00
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A Picture Paints a Thousand Words
5 Lectures 14:14

The aim of this video is to introduce the concept of data visualization and some of the tools available.

Preview 03:09

The aim of this video is to allow the user to create a simple visualization within R.

Creating Static Visualization Plots
03:46

This video looks at some tools to create interactive visualizations.

Creating Interactive Plots
02:01

The aim of this video is to publish the graphics created with the visualization tools in R.

Publishing the Graphics
02:06

The aim of this video is to introduce some of the concepts that have not been covered in this course.

What's Next?
03:12
About the Instructor
Packt Publishing
3.9 Average rating
8,138 Reviews
58,530 Students
686 Courses
Tech Knowledge in Motion

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

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