Understand the principles of R and make your data more efficient

R is a programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and data analysis.

R is a cost-effective tool tailored for all kinds of quantitative research and can easily be inserted in a complex toolchain. It’s thoroughly tested and has a supportive user community, so you can apply the community’s contribution to your analytics, helping you extend data processing and utilisation capabilities beyond functionalities provided by SPSS.

But getting to grips with R can be tough, even for seasoned statisticians and data analysts. This course, designed by Nebu, will help you understand the main principles, find out how to use it properly and make your data analytics more efficient.

Please bring your own laptop If possible. R and RStudio should be installed before the training (full instructions will be shared with attendees in advance – no additional costs are involved).

Learning outcomes:

  • Discover a brief history of R and what you can do with it
  • Understand the main principles of the R language
  • Learn what Packages are and how to use them in your day-to-day work
  • Understand the methods to prevent you from falling at the first hurdle in using R
  • Make your data analytics more efficient – optimising cleansing, processing, merging and visualising

Who will benefit:
The training is designed by Nebu for researchers and data scientists/analysts who are experienced with SPSS, looking for automation of research projects or looking for more data processing and analytical freedom to bring innovation to their projects.

Trainer: Simon Raper


The Old Trading House, 15 Northburgh Street,London,EC1V 0JR

Simon is an RSS accredited statistician with over 15 years’ experience working in data science and analytics and many more in coding and software development. His specialities include machine learning, time series forecasting, advanced statistical modelling, market simulation and data visualisation. In 2014 he founded Coppelia Machine Learning and Analytics whose clients include Google, J S Sainsburys, ITV, The Bodyshop, Direct Line Group, Citizens Advice, The Telegraph, Omnicom Media Group and The Trussell Trust. He has worked with: Channel 4, ITV, Mindshare, McDonalds, Unilever, Jaguar, News UK, Credit Suisse, Betfair and AOL. His blog on statistics, cloud computing and data visualisation has attracted over half a million views and his posts have featured in the online editions of The New York Times and The New Yorker. He is a regular writer on statistics for Significance magazine.

Additional Information

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