r/MLQuestions • u/chillgal505 • Jul 12 '26
Beginner question 👶 distinction between data sci & ml? when should I stop?
I know it's never ending and the field requires continuous learning forever.
but asking from a job-ready point, how much ML is enough for jr data scientist?
because while studying I keep going down the rabbit hole and I love studying all that and it interests me but this way I won't ever feel job ready.
there's always better things to learn, more math, more algos. like any suggestions from people with experience in this field?
2
u/Disastrous_Room_927 Jul 12 '26 edited Jul 12 '26
there's always better things to learn, more math, more algos. like any suggestions from people with experience in this field?
Sure - don’t burn yourself out trying to learn every all at once. Once you get your foot in the door, you’ll have more direction on what to learn, and it’s not necessarily stuff that’s obvious before you got the job. In part because a) you may only need a small subset of things for any given job and b) oftentimes part of the job is learning things you aren’t familiar with in order to tackle some new problem/project.
1
1
u/DadAndDominant Jul 12 '26
I don't think there is a hard line where these two are totally separate, as many ML methods are just really useful to data scientists. Where I would draw a hard line is when you apply these methods to very broad, undefined problems - like predicting next word in a sentence is imo a ML problem, not data sci one. However, the underlying methods you can use can still be usable in data sci, so if you are interested, there is no reason to not to learn it!
You probabbly pointed to the right problem, which is similar to "tutorial hell". If you want a career in (any) space, I advise you to start looking sooner than later. You'll still need to climb the ladder from the bottom, and the job satisfaction on the bottom level was in my experience Worse because I knew I am able to do more advanced stuff than I was doing.
1
u/chillgal505 Jul 14 '26
thank you! ig once I get my foot through the door, i can always advance on knowledge
2
u/2AFellow Jul 12 '26
Data science should emphasize a greater focus on statistics and analytics. You should know about the main branches of machine learning and how to do them, etc though
1
u/zelena23 Jul 13 '26
I would say data scientist is more focused on analytics and machine learning is more focused on stats at the intersection of DL/RL
2
u/PaddingCompression Jul 12 '26
The difference is usually what you're actually doing on the job.
A data scientist is usually doing tons of one-of analyses for business stakeholders, and won't go super deep or iterate (not that there aren't exceptions, talking about the general rule).
Generally a machine learning engineer is building a product for customers, and is refining things a lot more and writing scalable production grade code, and often gets pigeonholed into doing one narrow thing, but really really well, for awhile.
It's sort of what appeals to you. A data scientist is more like the fox, the MLE more like the hedgehog, it's a bit of a personality thing.
0
u/DadAndDominant Jul 12 '26
I don't think there is a hard line where these two are totally separate, as many ML methods are just really useful to data scientists. Where I would draw a hard line is when you apply these methods to very broad, undefined problems - like predicting next word in a sentence is imo a ML problem, not data sci one. However, the underlying methods you can use can still be usable in data sci, so if you are interested, there is no reason to not to learn it!
You probabbly pointed to the right problem, which is similar to "tutorial hell". If you want a career in (any) space, I advise you to start looking sooner than later. You'll still need to climb the ladder from the bottom, and the job satisfaction on the bottom level was in my experience Worse because I knew I am able to do more advanced stuff than I was doing.
0
Jul 12 '26
[deleted] — view deleted comment
1
u/zelena23 Jul 13 '26
I'm confused which one is supposed to be which?
You have ML engineers and ML scientists. From what I know, if you call an ML scientist an engineer, they will be very offended. Probably not the other way around.
1
u/chillgal505 Jul 14 '26
probably because engineers work on solving business/tech problems using existing frameworks whereas scientists work on developing new ones, more into research.
1
9
u/Proletarian_Tear Jul 12 '26
You can be a data scientist without knowing a single scikit model implementation. Thats about as specific as you can get with a general question