Programming Languages and Tools

MATLAB – A numerical computing environment that uses its own MATLAB scripting language to manipulate, analyse and visualise data. A large number of toolboxes exist that extend its functionality. The University licence includes approximately 70% of the toolboxes available in the MATLAB suite. [Need to purchase]

R – A programming language and software environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques. A large number of packages that extend the functionality of R for data analysis beyond the basics are available. R does have a steep learning curve, and so may take some time to pick up for those who are new to programming. [Free]

RStudio – RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. [Free]

Python – A general purpose programming language with a focus on simplicity and readability that make it relatively easy to learn for those with little programming experience. Python requires additional libraries to be usable for data analysis and visualisation (e.g. NumPy and pandas for analysis, and Seaborn or Bokeh for visualisation). Installation of Python through the Anaconda distribution is recommended, as it includes many of the most commonly used Python packages. [Free]

Jupyter Notebook – The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualisations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modelling, machine learning and more. The Notebook has support for over 40 programming languages, including those popular in Data Science such as Python, R, Julia and Scala. [Free]

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