This is a gentle introduction to programming for data analysis. The concepts presented here will be new for some of you, or you may already be well versed in coding. Either way it should hopefully get all of you to the stage where you can install and use one or more of these languages for your own data analysis needs.
There is no way we can teach you everything about coding in 2 hours, however we can give you the basic knowledge you will need to get started, and point you in the right direction for learning more.
So, we're going to cover coding basics, how to organise your work, and some of the main packages used by the different languages for things such as charting and data analysis.
At some point in your training or work, you will have come across one or more of the languages we will be discussing today, Matlab, R, and Python.
Each of these languages provide many of the features required to achieve the aims of your analysis, and the choice of them generally boils down to personal preference, what your supervisor or tutor used, and what packages are available for the language. Matlab is used heavily in engineering disciplines, R is primarily a statistics language, and Python is a general purpose language with many packages for scientific computing.
Despite their differences, in terms of data analysis, any of these will ultimately achieve your aims of opening and processing your data, analysing it, and recording the results somewhere. That's because all of these languages and packages utilise and are built on the same programming fundamentals, which we're now going to go through.
We recommend further reading and training. The Software Carpentry courses are available online and are recommended for more in-depth training on specific languages and technologies.