Github for data science

  • #1
244
6
Hi

I have the educational background to do a few jobs I have seen posted as "data scientist" or "data engineer". However, I keep getting told that to get a job like that with my background, in mathematics, I need to post code examples on Github. However, I always feel like my ideas are either too basic or too advanced (so, either it won't look good enough for recruiters or it'll take me forever to actually write the code). I thus never get started.

My question is, how good should my code be? Where should I start?
 

Answers and Replies

  • #2
Dr Transport
Science Advisor
Gold Member
2,491
613
Just put it out there. If you include the link in your resume and job application, they'll look at it and decide if it is what they are looking for in a programmer.
 
  • #3
rkr
Gold Member
18
17
Hi

I have the educational background to do a few jobs I have seen posted as "data scientist" or "data engineer". However, I keep getting told that to get a job like that with my background, in mathematics, I need to post code examples on Github. However, I always feel like my ideas are either too basic or too advanced (so, either it won't look good enough for recruiters or it'll take me forever to actually write the code). I thus never get started.

My question is, how good should my code be? Where should I start?

I don't think you need to post code examples on GitHub to appeal to your employer. I've gone through thousands of applications and actually, on some 90% of the applications where I've seen it included, it has actually hurt the candidate instead.

If you do go the route of including your GitHub profile, I personally feel that a better signal than your own repositories is to have meaningful contributions to large projects. This also gives you an easy way to generate ideas that are relatable to your recruiters - because surely, if it's an important issue to a large project, it's probably accessible to a wide audience. A way to find these is to look for issues tagged with "good first issue" or "contributor-friendly". Here's a few examples for Python: 1, 2, 3.
 
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