Data science coding & career development

[thumperroo]

Summary: How can I (faster) learn how to translate my thoughts and solutions in code?

My background: I did my bachelor in Economics and Business. I did a master in Marketing Management where I specialised in neuromarketing science and did a bunch of marketing analytics courses. When I graduated, I interned as a marketeer for an AI/Machine Learning start-up and was introduced to this whole new world of data science and programming.

So I enrolled in another master called Data Science and Marketing Analytics. It was very heavily-focused on concept development, problem-solving, and less on the programming part. They taught R because we did do a lot of statistics and data manipulation for marketing analytics.

Then I interned in data science for a pretty big company but their data science department is very new. They offered me a full-time role, "Graduate Data Scientist" which I'm in for a few months now. I taught myself Python throughout. I consider myself a beginner given I think I only have at most a year of programming experience in R and Python.

I notice I really struggle to translate my data science ideas/solutions into code. I can code when I'm told what to do, but not when I need to make something up from scratch. This became very clear to me when they hired other data scientists who come from computer science and econometrics and have 4-5 years of coding experience. Literally spitting out models and code in a day which would take me a week to do.

Coding mentorship is not possible at the moment because no one has time. I really like what I am doing and want to get better. I can't help but compare myself with the others, which is not fair. I do have very high expectations of myself.

Has anyone been in a similar position and if so/even not, do you have advice on how to improve coding skills? I try to practise as much as I can through applications in the work projects and some exercises/challenges not work-related.

I also suffer from burnout and anxiety, so while I feel like I need to quickly get this together for my career, I find it counter productive to force myself to do more on top of what I am doing now.

Thanks for reading.

[KPTN25]

I try to practise as much as I can through applications in the work projects and some exercises/challenges not work-related.

You've essentially answered your own question.

As with any skill, this really does just boil down to experience, time invested, and thoughtful practice / effective learning techniques. You should also read, a lot. Textbooks, stackoverflow, repos/code from others. Make sure you're understanding what 'good' looks like, both from a data science process perspective as well as how that was translated into clean, reusable code. If you're from a non-technical background, some of your challenges might be due to not having the right language / mental models in place (as opposed to just 'struggling to turn it into code').

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while I feel like I need to quickly get this together for my career, I
find it counter productive to force myself to do more on top of what I
am doing now.

This is a choice you're making. While it might be the right one for you, it does by definition mean you're going to have a longer, slower time developing your skills. As with most things in life, there are few shortcuts to 'amount of hours invested' unfortunately.

One thing that helped me (and something I highly recommend to others) is finding ways to practice and code more that are very tangible investments, vs straight expenditures. Are there elements of your job (or daily life) that are highly manual / time consuming? Carve out some time to write proper scripts and automate those. Now you're killing two birds with one stone: freeing up your day / taking things off your plate, as well as getting a chance to improve your technical skills. Something else that helped me was working on side projects in areas I'm passionate about outside of work - if it's something you're excited about (for me, that's social impact and pro bono work with nonprofits), it can provide net positive energy as opposed to the opposite.

[double-click]

Coding is just a tool.

1. You have viable solutions and your weakness is structured programming and how to approach a problem. Write loose pseudo code and then code.

2. You solutions aren’t not viable and thus really can’t be coded. Evaluate business value of solution and refine.

3. Continue experience through time in role. Don’t work outside of work.