When I was working on the basic concept of R Tutorials I had to face one decision at the very beginning:
Do I create one humongous course with over 10hrs content or
Do I split the R Tutorial content over several courses?
I decided for the later, creating a course tree structure and here is why:
Splitting up courses according to content allows you to choose which course to take. For example if you do not need in depth knowledge about ggplot2 plotting, it is easy to skip the ‘Graphs in R‘ course and just move on to the ‘Statistics in R‘ course. This strategy also allows my team and myself to adjust the course creation process after every final product, leading to better course quality.
On the top of that I can also quickly adjust new technologies, delivering fresh tutorials as soon as new technologies arise.
The course tree itself has a 2 component foundation. The courses R Basics and Level 1 contain all the necessary ingredients to bring you to an intermediate level in a short period of time. The courses are meant for total beginners, contain exercises and have a step by step approach that keeps you motivated and wanting to learn more.
As soon as you have a solid foundation you can branch out to specific fields. Graphs in R, Statistics in R, Text Mining, Scraping and Sentiment Analysis and Time Series Analysis and Forecasting in R are advanced courses. You need to be familiar with R in order to benefit the most. You can choose the order in which you take them or even skip one if you do not work with those tools all.
The Machine Learning course is even a more specialized course, which requires solid statistics knowledge, therefore it is recommended to do the Statistics in R course first. That makes sure that you have an optimal learning success.
You can always take a look at the course tree and check out which courses are best suited for your career and in which order you want to participate in them!