Information for incoming MSc students in statistics and statistical ecology


This information page has been set up for incoming MSc students who will be studying for one of the following:

  • MSc in Applied Statistics and Data Mining
  • MSc in Statistics
  • MSc in Statistical Ecology

The purpose of this page is to offer up material & information that will help you in the coming year. We look forward to seeing you all in September.

Programming (in R)

Students for our programme can have a wide range of backgrounds, in particular their computing experience. While we typically assume very little previous background, we do assume that you’ll be able to pick up skills quickly where needed.

In order to get the most out of your time here, you’d benefit greatly from having rudimentary programming skills under your belt at the start. One of the most useful programming languages for applied statistics is R. You will encounter others, but this is certainly a basic skill you should have.

If you need to get in touch with the post-graduate stats team, please contact: 

It helps to know some R

R is great

If you're embarking on an applied statistics career, R is almost assumed knowledge these days. Certainly, you'll need some flexible programming language to be useful.

  • You can obtain R here: R.
  • R is most easily used in conjunction with some compatible code editor, and we recommend the R-Studio IDE (Integrated Development Environment) which can be found here: R-Studio.

Some basic exercises

It is strongly recommended that you work through the following exercises, which indicate the level of R competency that will ease your way on our programme.

If you have encountered R before, then this is just a reminder of simple things you should know. If you are new to R, then this is an indication of where it is beneficial to be.

  1. Introduction to R part 1 (PDF)
  2. Introduction to R part 2 (PDF)
  3. Introduction to R part 3 (PDF)

Data Sets

  1. R data set 1 (Word)
  2. R data set 2 (Word)

Solutions to the above

  1. Introduction to R part 1 - solutions (PDF)
  2. Introduction to R part 2 - solutions (PDF)
  3. Introduction to R part 3 - solutions (PDF)

Resources for R

R is great. It's open-source and hugely popular within statistics and beyond. The amount of contributed material is incredible and there are many, many resources to help you learn. A few good ones to start with are listed below (but there are many more):