Number of lectures in series: 8 two hour sessions
Pre-requisites to joining: Completion of the Introductory taster session or some prior experience of the R software environment
This session builds on the Data Analytics in R Introduction (a prerequisite). You will learn to write code and conduct your own data analysis. The series covers additional data storage options, data structures, and how to leverage their features. Topics include data distribution, descriptive statistics, presenting summary data with tables and charts, as well as introductions to statistical testing, correlation, and basic machine learning using general linear regression.
After completing this series and the weekly exercises, you'll be ready to start your own data analytics projects in R and use a range of free online coding resources. The series will also show how AI can speed up your development. You'll also gain access to the 3rd lecture series, Machine Learning Introduction, which during 6 lectures, covers core machine learning concepts and examines their ethical and practical uses in organisations.
