R is one of the most used programming languages in Data Science to perform data analysis, used to visualize the data, to apply statistics and for machine learning analyses. This course is designed for participants with no programming experience and is the most beginner-friendly course within Innovation Scholars. We will be using example questions related to health research to learn how to write ad-hoc codes to manipulate and visualize data using R packages designed for Big Data analyses.

This course is for you if:

• You are a complete/near complete beginner to programming
• work in health-related sector as either clinical, administrative or academic staff - for example as a nurse, health administrator, consultant, biomedical researcher, etc.
• You require flexible learning format
• You need to start using more data for your current or future role
• You want to be able to perform basic tasks in R, such as making plots and sorting through tables, based on health-data without getting too deep into the theory of coding

Course delivery mode

This course is entirely self-paced and all the materials are on the Learning Hub module. We recommend dedicating a total of approximately 10 hours of learning time to get the most out of this course.

Live Q&A sessions with the instructors will be scheduled regularly so participants can receive support in whatever stage of the course they are. The dates will be emailed to you if you sign up to the course.

You will be going through 15 learning sessions. Each session:

• is carefully designed to be an immersive, hands-on learning experience of 30-60 minutes
• has a video lecture in which you are following the instructor in writing simple code and applying it to health-related questions.
• has a link to learning materials the instructor is using if you prefer to read
• offers you to attempt a small coding challenge based on what you learned before moving on

Technology required

You will need to be able to log into RStudio Cloud or download and use free the free software RStudio on a computer. This is to be able to take part in the parallel coding most lectures require and complete the challenges as part of the each session.