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.
- This course is for anyone, whether they are a hobbyist or professional data scientist.
|Section 1: Introducing R|
|1.1 The course overview||00:00:00|
|1.2 What is R?||00:00:00|
|1.3 The Structure of the Language||00:00:00|
|1.4 Data Structures within R||00:00:00|
|1.5 Writing a Simple Program in R||00:00:00|
|Section 2: Getting Data into R|
|2.1 The Structure of a DataFrame||00:00:00|
|2.2 Creating a DataFrame from a CSV File||00:00:00|
|2.3 Creating a DataFrame from a Zip File||00:00:00|
|2.4 Creating a DataFrame from a Database||00:00:00|
|Section 3: Cleaning and Blending Data|
|3.1 The Tools Available for Cleaning Data||00:00:00|
|3.2 Dealing with Null Values||00:00:00|
|3.3 Standardizing Date Formats||00:00:00|
|3.4 Blending Multiple DataFrames||00:00:00|
|Section 4: Cooking the Codebook|
|4.1 What Is a Codebook and Why Create One?||00:00:00|
|4.2 Creating the Codebook Using Standard R API Functionality||00:00:00|
|4.3 Manually Creating a Custom Codebook||00:00:00|
|Section 5: Data Mining and Analysis|
|5.1 Introduction to Data Mining and Analysis||00:00:00|
|5.2 The Tools and Techniques for Creating the Story||00:00:00|
|5.3 Regression Analysis with R||00:00:00|
|5.4 Clustering Data with R||00:00:00|
|5.5 Classifying Data with R||00:00:00|
|A Picture Paints a Thousand Words|
|6.1 Data Visualization Tools||00:00:00|
|6.2 Creating Static Visualization Plots||00:00:00|
|6.3 Creating Interactive Plots||00:00:00|
|6.4 Publishing the Graphics||00:00:00|
|6.5 What’s Next?||00:00:00|
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