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Learning Path: Data Science With R

Learning Path: Data Science With R

Make yourself comfortable in R and get deep into data science using R with this Learning Path.


Learn R and get comfortable with data science

Excited by the endless possibilities offered by the fields of data science and data analysis? Let R set you on your way!

Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way.

Make yourself comfortable in R and get deep into data science using R with this Learning Path.

About the Authors:

Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife. He can follow him on Twitter at and he periodically writes at
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 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.
Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.
Fabio Veronesi obtained a Ph.D. in digital soil mapping from Cranfield University and then moved to ETH Zurich, where he has been working for the past three years as a postdoc. In his career, Dr. Veronesi worked at several topics related to environmental research: digital soil mapping, cartography and shaded relief, renewable energy and transmission line siting. During this time Dr. Veronesi specialized in the application of spatial statistical techniques to environmental data.
Yu-Wei, Chiu (David Chiu)is the founder of LargitData (, a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, please visit his personal website at
Basic knowledge
Requires no programming knowledge - we’re covering basics of R too!

Course Information

Requires no programming knowledge - we’re covering basics of R too!

Get to know the basic concepts of R: the data frame and data manipulation
Get data from numerous sources such as files, databases, and even Twitter
Understand how easily R can confront probability and statistics problems
Work with complex data sets and understand how to process data sets
Evaluate k-Means, Connectivity, Distribution, and Density-based clustering
Create professional data visualizations and interactive reports
Create a codebook so that the data can be presented in summary

Anyone interested in becoming a Data Scientist

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Course Specifications

IT and Computing courses are available to study on our learning platform. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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