falsenine

The recent Ask HN side project thread is a reminder for me to learn new skills for 2022 that would give the capabilities to develop side projects that can be potentially monetized.

In terms of tech skills, I am currently learning R for basic inferential statistics for grad school. Otherwise, I have no other relevant programming knowledge--and I am not sure how one would monetize R "side projects".

For those with experience building side projects, what technical skills should I learn in 2022 that would enable me to create side projects? I think an initial step in learning Python would be useful. In setting these goals, I have to be aware that I'm still in grad school, and do not have all the time in the world, of course.

"Skills" is a broad word and can encompass many different things. Maybe technical skills related to medical side projects would be more relevant to me, but I'm also interested in other areas of tech.

Currently a masters student in physiology, in a non-technical lab. But would like to learn the technical skills required for understanding and applying deep learning (etc). I often see DL/ML/AI applied to fields such as radiology, dermatology, cardiology, and it would be great to not only understand this kind of research, but also be an active participant in the future.

As someone with a limited technical/quantitative background (currently learning R for a biostatistics course), what would be the ideal approach I should take to learn DL/ML/AI?

From my search it seems that there are two ways of approaching the learning roadmap/pathway:

(1) Practical approach: learn the right tools and apply them to the problem at hand. (2) Comprehensive approach: understand the mathematics behind DL/ML/AI so that you have a greater understanding of the tools.

I think many researchers in medicine, especially if they're coming from clinical backgrounds as opposed to Comp Sci backgrounds, use this practical approach. This approach would be more feasible for someone like me, whereas learning all the required mathematics would probably take several years of study (on top of my other responsibilities).

Ultimately, what steps should I take? I think Step 1 would be to learn Python. I am already learning R, but I see Python used more frequently.

Currently a masters student in physiology, in a non-technical lab. But would like to learn the technical skills required for understanding and applying deep learning (etc). I often see DL/ML/AI applied to fields such as radiology, dermatology, cardiology, and it would be great to not only understand this kind of research, but also be an active participant in the future.

As someone with a limited technical/quantitative background (currently learning R for a biostatistics course), what would be the ideal approach I should take to learn DL/ML/AI?

From my search it seems that there are two ways of approaching the learning roadmap/pathway:

(1) Practical approach: learn the right tools and apply them to the problem at hand. (2) Comprehensive approach: understand the mathematics behind DL/ML/AI so that you have a greater understanding of the tools.

I think many researchers in medicine, especially if they're coming from clinical backgrounds as opposed to Comp Sci backgrounds, use this practical approach. This approach would be more feasible for someone like me, whereas learning all the required mathematics would probably take several years of study (on top of my other responsibilities).

Ultimately, what steps should I take? I think Step 1 would be to learn Python. I am already learning R, but I see Python used more frequently.

Doing a masters degree in physiology, while not completely unrelated to programming, my research lab isn't one of those technical/quantitative ones. I am learning R for my statistics course, but I am interested in learning Python too.

I am wondering if it is feasible to learn Python (~1-2 hours a day) during grad school? My intentions in wanting to learn Python stem from my dreams of starting a startup. Is it unreasonable to have these startup intentions if I am busy with unrelated work?

Just want to get some opinion from the HN community, is it worth the effort to learn Python, or would my time be better spent sticking with R and using it for basic statistics for research?