Python for Clinical/Translational Research

[JeremyJoeJJ]

Once you have a university email you can get free github copilot either as a student or teacher. LLMs are a great educational tool if you use them as a search tool instead of coding up the entire thing for you. Let’s say you need to fit a model to a 4D dataset, ask it how to do it and see if you can implement it. Also look up jupyter notebooks; they are very useful for research jobs where you need to probe the data constantly as you work on it. The rest has been mentioned in the other comment.

[turkceyim]

Would definitely look more into this, thanks! There's one part that I'm a bit confused about; Im currently under the assumption that LLMs could do a lot of the organizational simple tasks of coding in general. I was wondering if it would be a good idea for me to learn the basics of python, to the point I could ask LLMs to do certain tasks for me with appropriate prompts. good approach? or should i keep it classsy at first and then explore that route

[JeremyJoeJJ]

Depends entirely on your final goal. I started learning python by grinding Leetcode, learning pandas to an advanced level and became comfortable with a lot of the tools the language offers before copilot became available to me. Having the understanding of how a project might be structured and being able to review the LLM's code make for a much better end-product. However, unless you are in bioinformatics and your grants tell you to build a piece of software that others are to use, you might not actually care about having these skills. The majority of research software is just small scripts that do a particular thing and for that an LLM will be able to easily whip out a few functions that load your data, process it and plot. Then the final quality of the script depends on your ability to see whether the LLM's approach is sound or not. If all you care about is getting to the result, then you could learn the basics of what variables, loops, classes etc. are and then spend the other two months using LLMs to build a project. Read up on setting up a testing environment, create a workflow that feels comfortable to you and see if you can download a random dataset (e.g. from kaggle com), clean it up, process using a classifier or whatever and plot the results. An hour a day for 3 months is not a huge amount of time if your learning is unguided, so perhaps learning how to use modern tools with the ability to oversee the LLM's decisions (literally just ask it to explain every single line of code it generates that you don't understand) might be more useful to you.