Training

The Innovation Scholars training is provided in three pillars, Health data science, ‘Omics and Artificial intelligence. You can mix and match various modules from each pillar and there is no need to complete all modules in one pillar. There is some overlap between teaching in each pillar so please read the module objectives to find the course which best suits your needs.
Pilar 1

Pillar one

Health data science

AI

Pillar two

‘Omics

Omnics

Pillar three

AI

Summary of Modules

Arrows indicate the suggested flow of learning.

Health Data Science

‘Omics

AI

C

Introduction to AI and Deep Learning

Coming Soon

C

Machine Learning

Now Live

Natural Language Processing

Coming Soon

Unix and IT infrastructure

Coming soon

NGS pipelines

Now Live

ATAC-Seq

Now Live

RNA-Seq

Now Live

NGS on Galaxy

Now Live

Sequence Alignement and BLAST

Now Live

C

Intermediate Python and
Software Engineering

Now Live

C

Which R module should I do? 

With multiple R modules this section should help you choose which module is right for you. 

Basic R with Data Carpentry is our most introductory course. You will be following video lectures where the instructor demonstrates step-by-step programming in R that you can follow along with on your own device (parallel coding). Even the most basic steps such as how to create and save your code will be covered. The lectures will provide you with all the information needed to be copied directly when working with your own data. This is a great option if you are not sure if R is for you. It is around 10 hours learning time in total including the hands-on coding and data analysis challenges.  

Introduction to R for Health Research sits half a step above this. While the module assumes that learners have no prior knowledge of coding you may have some experience in basic maths and basic statistical principles. This course is a more in depth offering covering much of Basic R and a little bit more. There are many opportunities to do your coding but to also experiment with the learning materials to apply programming theory to your own work. As it covers more content and spends more time covering the theoretical background to R, the learning time is closer to 20 hours.