Hello! I'm Celine. I am visiting from beautiful, sunny Santa Barbara, California. I do software engineering for an awesome company called Apeel Sciences. We develop plant-derived technologies that help extend the shelf life of fresh produce.
I just started in the software space, been at it for a couple of years now. I come from a background in data science. I hope to give back to the data science community by recommending good coding practices to make your data science models more reproducible, scalable, and robust.
hahaha well well. It is really fun though. Are you currently working in the field yet. We have about ten days left in our bootcamp and hopefully, i'm able to land a job in it.
I think writing unit tests for statement coverage and integration tests is the most important thing. You want to make really easy for yourself by writing a testing script that you can just run every time you have a new iteration to your model or the functions you use for preprocessing so that you know you haven’t broken anything and you can trust that your code works.
I’ll start writing some documentation to provide examples and make this concept easier to digest but for now a quick google search might help you (: Hope this helps!
Right on - in your experience, is it something that happens with a lot of data science teams? (The writing of tests, I mean.)
I'm a weird convert towards testing, if it isn't immediately obvious hehe, but I'm always surprised at just how few tests can be found out there sometimes.
No, I don’t see a lot of testing in the data science world (: I agree, I think there could definitely be a lot more of it. It would make writing data models a lot easier to scale, instead of building code and fix models. But I feel like a lot of data scientists aren’t taught how to write good tests and that’s why they’ve been able to survive without it. Are you a data scientist? Where did you learn how to test?
I’m based in Austin. I enjoy working with front-end technologies and continuously learning.
I'm also an avid German soccer fan. 🇩🇪 ⚽️
https://andrewbain.io
Python is the best! You can learn the tensorflow library created by Google. And you can easily follow good coding practices like domain driven design and writing unit tests and acceptance tests.
To manage databases I recommend SQL. PostgreSQL is a great way to learn because it is open source.
Hello! I'm Celine. I am visiting from beautiful, sunny Santa Barbara, California. I do software engineering for an awesome company called Apeel Sciences. We develop plant-derived technologies that help extend the shelf life of fresh produce.
I just started in the software space, been at it for a couple of years now. I come from a background in data science. I hope to give back to the data science community by recommending good coding practices to make your data science models more reproducible, scalable, and robust.
Thanks for having me! Excited to be here (:
Hi Celine,
the company sounds really interesting!
Best of luck with your work and thanks for helping the data science community!
Best,
Sam B
Thank you! Best of luck to you as well (:
Hello
Hey!
Hi
Welcome Celine !!
Thank you! (: you as well!
Hello
Hello. So nice to meet you. I lived in socal for about a 9 years. Hope to pick your brain about some languages hehe.
Haha you're welcome to, although I am also new to React and Javascript so not sure that I would be the right person to ask (:
hahaha well well. It is really fun though. Are you currently working in the field yet. We have about ten days left in our bootcamp and hopefully, i'm able to land a job in it.
Yay good luck!! I’m sure you will find one. React is the hot new language (;
I started about a year ago, so I am still fairly new 😎 but loving it so far!
Hey Celine!
Would be curious to know if there are any best practices for testing in data science models? What would that look like?
I think writing unit tests for statement coverage and integration tests is the most important thing. You want to make really easy for yourself by writing a testing script that you can just run every time you have a new iteration to your model or the functions you use for preprocessing so that you know you haven’t broken anything and you can trust that your code works.
I’ll start writing some documentation to provide examples and make this concept easier to digest but for now a quick google search might help you (: Hope this helps!
Right on - in your experience, is it something that happens with a lot of data science teams? (The writing of tests, I mean.)
I'm a weird convert towards testing, if it isn't immediately obvious hehe, but I'm always surprised at just how few tests can be found out there sometimes.
No, I don’t see a lot of testing in the data science world (: I agree, I think there could definitely be a lot more of it. It would make writing data models a lot easier to scale, instead of building code and fix models. But I feel like a lot of data scientists aren’t taught how to write good tests and that’s why they’ve been able to survive without it. Are you a data scientist? Where did you learn how to test?
hi
That is incredible :3 You are cool
I've been developing for almost a year and I hope to learn a lot with all of you.
Hello
Welcome!
Hello,I am param from India, I want to become a data scientist.
Can you suggest me,which programming languages I need to study.
Python is the best! You can learn the tensorflow library created by Google. And you can easily follow good coding practices like domain driven design and writing unit tests and acceptance tests.
To manage databases I recommend SQL. PostgreSQL is a great way to learn because it is open source.
Be welcome :)
I'm always excited when people show up with ideas that at some sort will help the planet. I'll be following you to watch the news about this tech!
I’ll keep you posted! (:
Oh, that is great.