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> Statisticians, economists, biologists, social scientists all learn and work with R They used to work with R. And the old generation of engineers used to work

> Statisticians, economists, biologists, social scientists all learn and work with R They used to work with R. And the old generation of engineers used to work with Matlab. The old generation still does. The new generation has been using actual programming languages, typically Python, since high school. They were the first wave of graduates in 2019 that specialised in a discipline and were also competent in software engineering. The old generation is going to be driven out of the job market by the new in the span of 5 years as they saturate the senior tier of their respective fields. How do you compete for a job when all you know is R and your discipline, against someone who's a full fledged software engineer who knows your discipline and can put models directly into production use?

bart_spoon 7y ago
This simply isn't true. There are more of them that know Python these days in addition to R, but as a recent graduate of a respected statistics graduate program, I can assure you that R is still the overwhelmingly preferred choice in the field, and also is in economics.
jointpdf 7y ago
So assuming this is true, what plan of action do you recommend for an “old gen” data scientist who is strong in math, stats, ML theory, R, dataviz, ETL, research, etc., but who is not by a long shot a “full fledged software engineer”? I will soon be competing against this new crop of statistician/engineer superhybrids you speak of. I know a fair bit of Python (mostly for ML/DL applications), bash, and just a smidge of HTML/CSS/JS (just enough tweak a front end demo via R Shiny). I’m OCD enough that I make every effort to write clean and reproducible code and unit test it as I go (is this TDD?). I can implement some stats algorithms (e.g. EM algorithm, MCMC) from scratch with a pseudocode reference, but I rarely if ever have the occasion to do that for obvious reasons. I understand the concept of computational complexity, though I don’t have any figures memorized. But I’ve never taken any CS course beyond Programming 101. I wouldn’t know how to navigate a complex production codebase. Embarrassingly, I know almost nothing about git. I’m 100% sure I’d get slaughtered in a whiteboard interview or similar. For that matter, I could easily get nailed on some holes in my core data science knowledge (cough SQL cough). So, do I rebuild my foundation around software engineering, or just patch up the obvious holes? Grind towards a management position and let my inferior skills rot away?
- FranzFerdiNaN 7y ago
  Learning git is never a bad thing. But if you encounter a company expecting you to be a software engineer, run away. You're not that, you're a data scientist. You wouldn't expect a software engineer to be able to recreate some statistical proof from scratch, as you're testing the wrong set of skills.

bart_spoon 7y ago

This simply isn't true. There are more of them that know Python these days in addition to R, but as a recent graduate of a respected statistics graduate program, I can assure you that R is still the overwhelmingly preferred choice in the field, and also is in economics.

jointpdf 7y ago

So assuming this is true, what plan of action do you recommend for an “old gen” data scientist who is strong in math, stats, ML theory, R, dataviz, ETL, research, etc., but who is not by a long shot a “full fledged software engineer”? I will soon be competing against this new crop of statistician/engineer superhybrids you speak of. I know a fair bit of Python (mostly for ML/DL applications), bash, and just a smidge of HTML/CSS/JS (just enough tweak a front end demo via R Shiny). I’m OCD enough that I make every effort to write clean and reproducible code and unit test it as I go (is this TDD?). I can implement some stats algorithms (e.g. EM algorithm, MCMC) from scratch with a pseudocode reference, but I rarely if ever have the occasion to do that for obvious reasons. I understand the concept of computational complexity, though I don’t have any figures memorized. But I’ve never taken any CS course beyond Programming 101. I wouldn’t know how to navigate a complex production codebase. Embarrassingly, I know almost nothing about git. I’m 100% sure I’d get slaughtered in a whiteboard interview or similar. For that matter, I could easily get nailed on some holes in my core data science knowledge (cough SQL cough). So, do I rebuild my foundation around software engineering, or just patch up the obvious holes? Grind towards a management position and let my inferior skills rot away?

FranzFerdiNaN 7y ago
Learning git is never a bad thing. But if you encounter a company expecting you to be a software engineer, run away. You're not that, you're a data scientist. You wouldn't expect a software engineer to be able to recreate some statistical proof from scratch, as you're testing the wrong set of skills.