Do you find statistics confusing? Learning statistics doesn’t need to be difficult. In this course, you’ll be introduced to a completely new way of approaching the subject. Instead of trying to learn formulas and trying to understand the complicated mathematics that underpin statistics tests, in this course, you’ll learn to approach statistical problems by understanding the “question” that you’re trying to answer.

Dr Martin’s approach to teaching statistics is refreshingly simple. By understanding the nature and structure of your data, it will become blindingly obvious what statistical questions you can ask. By doing this course you’ll become very comfortable with data types and when to use the various statistical tests like the student’s T-test, Chi Squared, ANOVA and correlation tests. The course also provides some strategies and practical advice as to how to manage things like missing data.

You’ll also learn about research and study design. For example, you’ll learn about the difference between a Case Control and a Cohort study and how Randomised Control Trials are used to control for confounding. And as an added bonus you’ll get a brief introduction to R programming - a fantastic (and free) platform used for statistical analysis.

This course is for beginners or for anyone who has found statistics confusing and inaccessible. The underlying idea is to focus on the basics. Make you that you understand the question that you’re trying to answer and which statistical tests are most appropriate for the job. Once you have that done and dusted, you’ll find the rest of statistics pretty easy and intuitive!

Finding R difficult? It doesn’t need to be. This course will walk you through the basics in a way that is easy to follow. Dr Martin teaches with examples that use datasets that are already built into R Studio, so you can replicate the code at home.

The trick with R programming is to start with the basics and have a clear analysis plan. It should look something like this: 1) import and inspect your data, 2) clean and shape your data, including selecting variables, filtering rows and dealing with missing values, 3) describe your data, including tables that summarise key parameters, 4) visualise your data, and 5) undertake appropriate statistical and modeling analysis.

This course will give you a general understanding of how R programming works. You’ll find it easy and in fact exciting to work in R once you’ve overcome the initial fear of programming. It makes a lot of sense to do data analysis using R because you’ll have a clean record of how you drew your conclusions. Working with R also facilitates collaboration with others (so you can get help from experts very easily).

If this is your first step into the world of R programming and Statistics - welcome aboard. You’ll love it. So don’t delay, sign up now and start the adventure!