Worth learning OOP as a Data Scientist?
Hello blind.
I’m less than a year into my new role as Data Scientist with no other technical background. I create, tune, analyze models in functions and nested functions. They get me the results I want and I just continue to improve it from there. My peers are satisfied in my coding (aside from adding too much info and cleaning up the code). I think there are lots of room for improvement in coding efficiency and thinking about the next skills to learn/improve on. I see OOP is something more advance and definitely not my strong suite. I only know Python and R, so no other programming experience as well. I know coding is just part of the role, but I just want to see how much effort I should put into learning this new skill and changing the way I code.Better to start now than later in my career.
For current DS & MLE, do you use OOP much in your coding? Would you say one style is better than the other for this work? Or is the output more important? Or am I thinking about this incorrectly? Please explain. I’m still a noob.
For SWE & other developers, how do you feel about this topic? What are your interactions with DS in the company in terms of their codes? Does it matter to you if a DS utilizes OOP or not?
TC 80k
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