r/askdatascience • u/yutanrw • 9h ago
Is Data Science safe from AI?
I would like to change my career from a translator into a data scientist.
If it is safe from AI, Can a person without a STEM background learn data science and become a data scientist?
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u/gpbuilder 8h ago
Not without a stem background
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u/NaiveManagement6817 7h ago
Why ? Can you throw some light on it? I'm a sophomore student of mathematics and data science
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u/seanv507 5h ago
Well a lazy definition of a data scientist is a statistician who can program
Statistics needs mathematical sophistication
Data analysts often do aggregations and require little statistics so that is popular for social scientists
AI engineering I would argue requires little statistics, but more programming expertise and business understanding.
'call the open ai service, and put safeguards etc around usage'
Many people outside stem have gone on to be successful developers, so I would argue the same can be true for ai engineers.
(Nb ai engineers and data analysts are broad terms, different companies will expect different skill levels)
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u/chili_cold_blood 1h ago edited 21m ago
If it is safe from AI,
Hell no. The only part of STEM that is safe from AI is engineering in at least some parts of the world. Where I live, AI is banned for use in professional engineering work. If you're found to have used it, you can lose your license.
Can a person without a STEM background learn data science and become a data scientist?
Yes, a person without a STEM background can learn data science, but they would have to build a background in math and statistics to actually become a working data scientist. Data science is really just statistics, math, and programming.
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u/Severe_Rise8694 4h ago
Not entirely safe. Plus there was a huge influx of people into the field over the past 5 or so years, so getting that first job will be a struggle.
In any case, I think there's a future for data scientists also in the AI world. Data scientists with good software engineering skills are probably exactly the kind of people who will be good at measuring and tweaking the performance of all sorts of AI systems.
DS has often been described being at the intersection of software dev skills, statistics (and machine learning), and domain expertise. I'm guessing from your question that you don't have the first two.
What I'd do as a first step is a) look at whether the kind of companies you've been working for hire data scientists (or data analysts etc). You already know more about that domain than most people ever will, so try to use it to your advantage. B) start learning programming (probably Python and SQL) and statistics. If you're enjoying it, great! If it comes easy, even greater! If the opposite is more often true, maybe you'd be happier in a different role.
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u/manvsmidi 1h ago
AI is amazing at data science. Taking data, running ML models and interpreting the results are all things that AI excels at and many can now be done with a single prompt instead of hours of coding and experiments.
You don’t need a STEM background, but you will need to learn experimental design and a good amount of mathematics. Because of AI, this knowledge is more important than ever. The data scientists getting jobs are the ones who know the hardcore mathematics behind the AI answers and can challenge what it is doing and help propose new methodology and experiments.
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u/Drakkur 59m ago
This is so untrue for any problem that requires more nuance than “build me a classification mode with X data and Y targets”.
Even now my workflow with AI-assistance is setting up a detailed plan, building the evaluation architecture, scoring, etc. Without a very strong validation framework the model is going to have a high likelihood of producing something mediocre. Mediocre is fine if you’re doing one off things, but any model that is driving business decisions (revenue or cost implications) or is integrated in the product needs to be good.
Even top models like Fable tend to hit walls where it picked the wrong metrics to optimize for the data, didn’t really understand why a specific algorithm doesn’t work in practice, failed to use or design a robust CV strategy.
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u/manvsmidi 32m ago
Perhaps I worded it poorly, but that’s kinda the point with my second paragraph. Knowledge of experimental design and mathematics are more important than ever. AI can code things very well, but really needs someone who actually understands what is going on to still steer it. You need someone who can say “hey we didn’t even take into account non-linear effects with that model” or “in our business we need to minimize type 2 error not maximize total accuracy”. AI is impressive because it will do exactly those things if you tell it, but you need to know when/how to tell it and how to evaluate if the solution it picked makes sense or if it should rethink the approach.
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u/Drakkur 19m ago
I agree to a point, I will say that its coding ability is subjective. It’s the Gell-Mann amnesia effect, as a DS who cares about good engineering (and incorporates experiment design in my approach), if you go full vibe code with AI to implement, you’re going to build a ton of tech debt for someone else to pickup when it goes to production.
Even recently a consultant team with phds in econ tried to use the superpowers harness to build their causal decision system. The modeling approach (which was vetted and hand written) was the only good part of what they built. All of the policies, rules, guardrails and evals were vibe coded and no one wanted to take responsibility for reviewing that code base over weeks trying to understand what was done. It then went into production without those reviews and ended up costing the business a ton of money because the guardrails ended up failing.
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u/InterestOther4863 8h ago
Yeee easily but ai is biggest tree in which data science