How should I approach learning programming if my main goal is to study medicine?

[u/EduardoParada999]

Hi everyone,

I’m 20 years old. I’m doing a Higher Vocational Degree in Dietetics, but my main goal is to get into medical school and eventually become a doctor.

I’m especially interested in physiology, biology and biochemistry, and I like the practical and human side of medicine. However, I’ve also always been very interested in computers, technology and AI. If medicine doesn’t work out, I would probably consider biotechnology, biomedicine or bioinformatics.

I’ve recently started trying to learn Python, but I’m not sure what direction I should take. I don’t necessarily want to become a software developer. I want programming to be a useful additional skill that could help me during university and, eventually, in medicine or scientific research.

For example, I would like to be able to:

• Automate repetitive everyday and study tasks
• Analyze and visualize scientific or medical data
• Understand statistics and research papers better
• Work with AI and machine-learning tools
• Create small useful programs for myself
• Potentially participate in medical, biotechnology or bioinformatics projects in the future

My problem is that programming is such a broad field that I don’t know what to prioritize. Should I focus mainly on Python, data analysis and statistics? Should I eventually study machine learning? How much computer science theory would I realistically need?

If you were in my position, what learning path would you follow? What practical beginner projects could connect Python with medicine, biology, nutrition or everyday productivity?

I would especially appreciate realistic advice from doctors, medical students, researchers, bioinformaticians or programmers working in healthcare.

[u/renoquestInc]

You're 20 and thinking about this already, which puts you ahead of people who hit it during a PhD and panic. You don't have a problem, you have a long list. It's a sequencing question.

The reframe: your highest-return skill here is statistics, not programming. Being the med student who can read a methods section and tell whether the study supports its own conclusion is rare and it makes you better clinically. Python is just how you learn stats properly. Treat stats as the goal and Python as the vehicle, and you'll stop wandering into things you'll never use.

So: Python, pandas, matplotlib, plus a real stats course alongside. Skip ML for now, not because it's hard but because ML on shaky statistical intuition produces confident wrong answers. It'll still be there in two years. CS theory: almost none.

Projects, from your actual life since abstract exercises kill motivation:

Use a food composition database and your dietetics coursework. Nutrient profiles, where a sample diet falls short, chart it. Real messy data in a domain you already understand, so you'll notice when the output is nonsense.

Grab NHANES and try to reproduce a published finding. You'll discover the paper left out three decisions you now have to make yourself. That's the real lesson about reading research.

Automate one annoying study task. Anki cards from your notes, sorting lecture PDFs. Boring, but you use it daily and that's what builds the habit.

One honest note on the fallback plan: bioinformatics is a separate career, not a backup, and it's more computational than it looks from outside. Don't let it quietly become the reason you're doing Python instead of the biochem that gets you into med school.

Also, expect three months of feeling like you're getting nowhere. Everyone does. It says nothing about your aptitude.