Programming Languages

We suggest becoming proficient in the same language as your direct supervisor (start by choosing 1 language)

	Python	MATLAB	R
I’m stuck. I don’t know what to choose!	In general, we suggest asking your supervisor what they use and start there. However, here are some other things to consider: Python is extremely commonly used. If you see yourself as a hobbyist and like to run personal projects, it is great to learn Python. If you want any kind of ‘tech’ career, Python is an absolute must (and JavaScript, C#, Ruby etc. though Python makes it easier to learn these). In neuroimaging, a lot of newer libraries are being written in Python. MATLAB is the most ‘specialist’ of the languages, as it was designed for numerical computing and is mostly used in the science and engineering fields. In neuroimaging, a lot of the foundational libraries are written in Python. If you are an honours student, you will need to use R for your coursework and so it might make sense to carry over what you learn for your data analysis and visualisations. It’s more specific than Python, as it has a strong focus on statistical models and wrangling/visualising datasets. It is also used widely in industry and government because it’s free and simpler than Python. Most people really like RStudio (a graphical user interface (GUI) that is technically a separate entity from R) as well. Most people who used to use SPSS have now transitioned to R. R is the least commonly used in neuroimaging.
Principles and Philosophy	Zen	The strengths of MATLAB include extensive data handling and graphics capabilities, and advanced algorithms. This includes fast numerics for linear algebra, a large number of domain-specific built-in functions and libraries (e.g., for statistics, optimization, image processing, neural networks), easy generation of various kinds of visualisations of your data and/or simulation results.	R for Data Science by Hadley Wickham ‘Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R’.
Is it open-source	Yes. Python is an open-source, general-purpose programming language run by a US not-for-profit called the ‘Python Software Foundation’.	No. MATLAB is a language but it is also a proprietary computing environment (software). It is owned by Mathworks and Monash provides access to all staff and students. Octave, Scilab, and Julia are open-source alternatives, but are less popular.	Yes. R and RStudio (a downloadable development environment/software that most people prefer to use when running R) are open source developed by some friendly kiwis :)
Getting Started	Using Python on MASSIVE Option 2 - Download Python on your device 1st step - Download the language here 2nd step - choose your IDE (integrated development environment - software that you’ll use to interact with the code and run your code in). We recommend on of the following: PyCharm - freely available here Spyder - you can access through Anaconda - freely available here	Option 1 - MASSIVE via GUI Login to MASSIVE desktop Go To Applications > General Scientific > MATLAB Alternatively via Desktop Login to MASSIVE desktop Open terminal Type module load matlab/r2022b Type matlab & Option 2 - Download Matlab on your device Available on the mathworks website using your Monash account. See the Monash Software Catalogue for more detailed instructions You will need approx 4GB available	Use RStudio on MASSIVE Download R and R Studio Download R-4.2.3 for Windows. The R-project for statistical computing. RStudio Desktop - Posit Use RStudio - R sits in the background without much influence
Introduction: Introduction to variables, loops, statements etc.	Start with a simple self-paced course (intro to variables and if statements) ~1 hour. This will give you a very brief introduction to python and programming Alternatively, try this intro to Python for data science course ~4 hours This covers a bit more, including functions, and an intro to stats in python Introduction to plotting and data visualisation in Python via Matplotlib Most other plotting tools (including ones listed below) are built upon Matplotlib, so these are important fundamentals Or this more comprehensive course on python for neuroscience This course does not include videos It covers basics from variables and loops, to data manipulation and statistics. It might be good to check it out to fill in the gaps in your knowledge	Choose 1: Start with introductory materials from MathWorks ~1hr: Get Started Alternatively, Mathworks has the same content presented with videos ~1-2 hours For a more technical introduction, add on 1 of these: 1 hour YouTube course Long format MOOC from EPFL	For loops and if statements: How to Use If-Else Statements and Loops in R – Dataquest Functions R - Functions Intro to R: An Introduction to R 1 Introduction | R for Data Science
Applications in neuroimaging	Start here: Introduction to MRI in Python Choose the following as needed: dMRI analysis in Python fMRI analysis in Python sMRI analysis in Python Plotting neuroimaging data via nilearn.plotting, including plotting of: Volumetric data Surface data FC matrices fMRI carpet plots MRI preprocessing pipelines in Python (warning: more complex than previous resources) Gradient analyses in Python (also includes examples for loading and plotting fMRI data and FC)	Our lab’s introductory tutorial to working with connectomes ~1 hour Network Based Statistic (Brain Connectivity Toolbox) for analysing all edges SPM for fMRI analysis Gradient analyses in MATLAB (also includes examples for loading and plotting fMRI data and FC)	R is not commonly used in neuroimaging contexts - but there are packages out there (e.g. ROBEX for ‘robust brain extraction’) built on R. Here is a good walk through on using some R packages for neuroimaging analyses: Neuroimaging Analysis within R However, there is also FSLR - a wrapper of FSL for R (it is more common for FSL to be used outside of R) https://journal.r-project.org/articles/RJ-2015-013/RJ-2015-013.pdf Otherwise - R is great for cleaning dataframes with psychological data (e.g. Likert scale based depression instrument data) Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1) https://towardsdatascience.com/data-cleaning-in-r-made-simple-1b77303b0b17
	For further reading as you develop your skills
Advanced techniques	Machine Learning in Python Semi-introductory course to Python with an emphasis on ML	MATLAB Programming Techniques Numerical Computing Undocumented tip and tricks	Beginner’s guide to machine learning in R (with step-by-step tutorial) | R-bloggers
Other notes	Python Cheat Sheet Basic guide that covers types of variables, lists, dicts, functions, numpy, matplotlib, etc	Blogs/Pick of the week MATLAB on YouTube	8 R Programming Tips and Tricks That Will Make You More Efficient | by Adejumo Ridwan Suleiman | Level Up Coding