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:
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.
The aim of this video is to introduce the R programming language.
The aim of this video is to start with some important basics such as function and variable declaration.
The aim of this video is to introduce the different types of data structures available.
The aim of this video is to show that R scripts can be run from outside of the R IDE.
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.
The aim of this video is to introduce the concept of creating a data frame from a CSV file.
The aim of this video is to introduce the concept of ingesting data from a compressed file into R.
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.
The aim of this video is to allow the user to start cleaning datasets.
The aim of this video is to understand the process available to deal with missing values from a dataset.
The aim of this video is to look at the date format and process time.
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.
The aim of this video is to allow the user to understand the importance of a codebook.
This video looks at creating the codebook from standard r functionality.
The aim of this video is to create a codebook using standard R functionality.
The aim of this video is to allow the user to understand what data mining is and the steps involved.
The aim of this video is to begin the process of creating the data story
This video looks at creating a linear regression model.
The aim of this video is to look at clustering of data.
The aim of this video is to introduce the concept of classifying data within R.
The aim of this video is to introduce the concept of data visualization and some of the tools available.
The aim of this video is to allow the user to create a simple visualization within R.
This video looks at some tools to create interactive visualizations.
The aim of this video is to publish the graphics created with the visualization tools in R.
The aim of this video is to introduce some of the concepts that have not been covered in this course.
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