What is the course about?
R is a programming language used widely for statistics, machine learning and data mining. Its popularity has increased a lot in recent years, and it is particularly useful for business and science. This course is designed for those who have little to no programming experience, and would like to learn a computational way of thinking as well as a very useful language. If you have experience in another language and would like to pick up R, you will be provided with extra material suited to your level.
What will we cover?
For the first half of the course we will cover the basics of writing code in R. This will include concepts like data types, operators, lists, variables, data frames, functions, loops, and importing data.
For the second half we'll move on to how this can be applied. We'll have a look at how to use R for statistics, graphics, data mining, machine learning and time series. To make it more interesting, we'll use real life examples from science, finance, and everyday life.
What will I achieve?
By the end of this course you should be able to...
Solve simple problems using R programming. This could include analysing business data, or using data frames for handling large data sets. Being a good programmer is all about experience, but this course will give you the skill set to tackle new problems on your own. You will learn how to handle syntax errors and what to do when you get stuck.
What level is the course and do I need any particular skills?
The course is introductory, and no programming experience is required. Basic computer skills are required.
How will I be taught, and will there be any work outside the class?
You will be introduced to a topic, then you'll try programming it. There will also be exercises and games so you're not sat still staring at the screen all day. There will be an optional, but highly recommended homework set.
Are there any other costs? Is there anything I need to bring?
It is a good idea to bring a pen and a notebook. You might also want to bring a memory stick, or have a functioning email account so you can send your R files to yourself.
When I've finished, what course can I do next?
Introduction to Python, Data science with Python, Excel:analysing data, Data analysis with Power BI.