What field of research is the most coding heavy and most productive?

[and 99 others]

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I'm looking to learn how to code for the next 9 or so months and so would like to do research in a field that would be conducive for that. I have absolutely zero coding/programming experience but several years of research experience. I don't really care what the content of the research is, I would just like to use it as a means to get good at coding. It would be great if that field was also fast moving/productive, allowing me to produce data/posters/papers within that time frame.

Would love any suggestions!

 

[TheDataKing]

Just taking a guess but maybe epidemiology?

 

[star-lily]

From my own experience, I've found it extremely useful in epidemiology, radiology (image analysis, AI), neuroscience, cancer research. Coding in research is pretty ubiquitous tbh - I suggest picking up general skills in three languages (Python, R or Matlab, and Excel) to start since you can use these to accomplish pretty much any task you may come across.

It's great that you want to learn to program! We need more doctors who can.

Wow! Do you have any recommendations for learning resources/what worked for you? I know some Python, but it's very easy to get stuck down rabbit holes...

 

[Ell19]

definitely epidemiology, usually research assistants in those fields go into the position with a knowledge and baseline of how to code, so I am not sure how that would work for you

 

[and 99 others]

I can spend full time or close to full time hours every week dedicated to whatever the project is. In you guys' experience, would this level of work allow me to make meaningful progress in learning to program and producing a good amount of data?

 

[mickmouse301!]

neuroimaging!!!! i am biased, but it is the best 😉

 

[deleted1011020]

health data science

 

[IlDestriero]

Lots of departments have a bioinformatics division for data mining, QI projects, etc. I would think coding would come in handy there.

 

[deleted1011020]

Wow! Do you have any recommendations for learning resources/what worked for you? I know some Python, but it's very easy to get stuck down rabbit holes...

Python is the go to due to all the premade libraries available. You could technically use any (like I prefer Swift) but be ready to have to research a lot of math and know data structures/algorithms/databases extremely well to be able to sift through the info, perform analysis', and basically write the methods/functions/classes from scratch.

 

[EdgeTrimmer]

computational biology/chemistry/neuroscience

 

[Andrew Baines Bernard]

From personal experience, bioinformatics is huge in cancer research. Petabytes of data being uploaded constantly to genomic databases looking at specific cancers. Like everyone else has mentioned, learning R or Python would help you tremendously.

Also, try familiarizing yourself with Unix/Linux systems. If you're crunching all this data, you'll often need to use a high-performance computing cluster (if you can get access), many of which run off of Unix.

 

[HopeP]

...and know data structures/algorithms/databases extremely well .....

That is CS foundation

 

[deleted1011020]

From personal experience, bioinformatics is huge in cancer research. Petabytes of data being uploaded constantly to genomic databases looking at specific cancers. Like everyone else has mentioned, learning R or Python would help you tremendously.

Also, try familiarizing yourself with Unix/Linux systems. If you're crunching all this data, you'll often need to use a high-performance computing cluster (if you can get access), many of which run off of Unix.

macOS would be the most beginner friendly in my opinion given the learning curve to set up some linux distros + hardware compatibility.

 

[deleted1011020]

That is CS foundation

Yea but taking mathematical models and implementing them isn't. I think maybe I worded it wrong but thats more or less what I was referring to would be extremely challenging.

 

[HopeP]

Yea but taking mathematical models and implementing them isn't. I think maybe I worded it wrong but thats more or less what I was referring to would be extremely challenging.

I can relate being a parent of CS junior and pre-med sophomore.

 

[and 99 others]

Would I need an especially powerful laptop for learning R or Python? And do I need to pay for any of the software or anything?

 

[Andrew Baines Bernard]

Would I need an especially powerful laptop for learning R or Python? And do I need to pay for any of the software or anything?

No, if you're just learning the basics, you shouldn't need anything too powerful. And if you're processing massive datasets, they're usually pretty heavily regulated (tons of confidential clinical pt data, which is technically owned by the NIH and NCI), so your institution would either provide you with a secure computer, or you'd be utilizing a cluster with much greater capabilities than your average consumer machine.

As far as software cost is concerned, R, RStudio, and Python are all free to use.

 

[EdgeTrimmer]

Check to see what research is available at your school and going thru individual labs web pages gives an idea about not only the research they are doing but may also give the tools they are using. Also, see if they take undergrads. Once you have that info you can focus on what to learn and which lab to target.

 

[deleted1011020]

Useful for everything from neuro to ophthalmology. I think that’s a really cool field to be involved in

Theres a ton of work being done for pathology thats really interesting as well

 

[Medic741]

I'm looking to learn how to code for the next 9 or so months and so would like to do research in a field that would be conducive for that. I have absolutely zero coding/programming experience but several years of research experience. I don't really care what the content of the research is, I would just like to use it as a means to get good at coding. It would be great if that field was also fast moving/productive, allowing me to produce data/posters/papers within that time frame.

Would love any suggestions!

Metagenomics. There’s a lot to do with the datasets when you produce a taxonomy & phylogenetic distance table