Learn the fundamentals of R through downloadable Notebooks, discover data management tools & techniques, statistical packages, and explore regression analysis, building your first model, validation, and visualisation.
If you’re looking to learn the foundations of R, this course is the ideal place to start. An individual subscription gives you 3 months’ online access to:
As Well As
Our Industry and Actuartech Resource Libraries which feature curated additional content to assist you on your data science journey.
You can also request to do the online assignment for an additional fee; and if successful a course completion certificate could be issued.
“Foundations in R for Actuaries” introduces students to the data science pipeline whilst teaching them the fundamentals of the open source programming language, R.
Throughout this course, students are exposed to data science topics such as data cleaning, data processing, model building, and visualisation, as well as ethical and wider business considerations when using data science in practice.
This course is presented through our training platform using Notebooks, with the code and explanations embedded. Notebooks are downloadable, offering students the opportunity to code along on their own device, or edit the Notebooks. Students can run the code and make their own tweaks to see how it affects the output.
In this course, we consider training and testing Generalised Linear Models (GLMs), and validate the results, as this is easily facilitated by R. R has robust statistical capabilities allowing users to easily fit a range of models from standard GLM’s through to neural networks.
Chapter 1 introduces Problem Specification, beginning with an overview of R. It highlights its ease and functionality through using R as a calculator and implementing a simple linear regression model and plotting it.
Chapter 2 covers Data Collection which addresses importing external data and how to use different data structures.
Chapter 3 on Data Management showcases how to write purpose-built functions to manage data, and transform and manipulate a dataset in preparation for model fitting.
Chapter 4 outlines Model Building using GLMs and show cases some of R’s statistical functionalities.
Chapter 5 on Visualisation shows students how to use a variety of statistical functions to produce some basic graphs which assists in understanding the data better and validating the models.
The Appendix contains additional reading and references to some of the packages discussed.
We have tailored packages available to ensure that corporate teams have the option to attend structured live lessons by our tutors, and the option to request a practical assignment and bespoke feedback. Invoicing option available.
