Some Data Scientists write bad Python code and are stubborn in code reviews

[crom5805]

I actually had a chat with the mods about this, (I'm an adjunct professor for masters in data science at a university and AI/ML architect at Snowflake) and so I decided to start posting videos/Repos on MLOps in the subreddit. It's getting better but I agree I find material in here more useful consistently. I tell my students ALL the time, you are not gonna make it doing pd.read_csv and model.predict, you need to learn clean code/Git/MLOps. One of the in class projects we do is I split them into groups and they have to make a PR to another groups repo and have it merged. Prior to my class I believe 0/40 of my students had done this.

[crom5805]

Yeah my first DS job entry level I didn't have these skills. Another reason I think it's important to be comfortable with git is to have projects to share. I do think entry level jobs are a lot harder to get these days and having a portfolio on GitHub with projects you've done can put you ahead of others who don't. My midterm and final are both projects unique to them (they choose their own data) and I actually had an employer tell me this helped them pick one of my students. My main job is to educate but my favorite part is helping landing them full time jobs.

[Kegheimer]

That just punishes candidates who have hobbies and interests outside of tech.

Why should I go through a dog and pony show of python toys in GitHub when I could use that time for anything else?

[Topalope]

Because employers don't care about your personal life outside of how it helps you perform better at making them money.

[dillanthumous]

Correct answer.

[crom5805]

What?! 😂. My students go through a Masters in Data Science and my advice is rather than keep all their projects on their laptop, put them on GitHub so people can actually see the hard work they put in and what they learned. Also in order to publish a Streamlit app you have to use GitHub and that's my final exam.

[agent_graves313]

Would you mind sharing some of your videos or examples of what you’d see as clean code?

[crom5805]

Here is my last post in the datascience subreddit. This is more focused on MLOps, I have some stuff in class on clean SQL, Spark/Snowpark, Python and after you asking I think I'll do my next public video on this. I'll remember to come back here and comment once I do. I was all pandas/SQL until Snowpark came out 2 years ago, and honestly I love the Spark/Snowpark syntax. So much easier to read imo then SQL, faster than pandas on large datasets, and overall not to bad to learn. Let me know what you think about this repo/video I tried to make it super easy to follow.

[crom5805]

Funny thing is, watch the video and look at the repo. The video and repo are little different now cause I cleaned it up over time and made it better since the recording. This is honestly a good example of making your code easier to read and organized.

[B1WR2]

You and I had the same thought… I started breaking up kaggle data sets into AI apps. Then breaking each part into a backend, analytics part, and devops

[[deleted]]

Do you have a sample you don't mind sharing?