A wide variety of coding languages are used, and every field has its favorites. However, for bioinformatics-related applications (like analyzing sequencing or proteomics data), two standouts are good choices for novice coders: R and Python.
When I started to learn how to code, I used a mix of different resources to learn what I needed. By far my most useful resource was a grad student in our neighboring lab who is an expert in R.
If in-person courses aren’t available, there are many online training courses (both free and paid) to learn everything from the basics of “What the heck is coding?” to specifics like “How do I make a heatmap from my RNAseq data?” Online tutorials are great resources because they almost always have practice datasets for you to use.
As I mentioned above, my best training resource was a fellow grad student. Learning from others who know what they’re doing is a fantastic way to polish your skills and get constructive feedback.
Let’s say you are planning to go on a month-long trip to a foreign country where people don’t speak your native language. There’s no need for you to quit your job, study that language every day, and become 100% fluent before your trip.
Coding is hard, especially for beginners. It’s an entirely different way of thinking that’s not always intuitive to people who have spent their lives in the world of biology. But remember that it’s perfectly normal (and even good) to “fail” at programming.
One thing is a given, whatever your level of expertise, you are going to have to look up how to do things from time to time (or all the time, when you are a beginner). It is expected that with experience
Yes, as a bioinformatician you can spend more time working from home (which used to be a thing of joy before 2020), but that is not what I am talking about here. For me, the best way to learn something has always been by doing it.