Wanting guidance for tech stack of data science

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James Benett

Updated 2 days ago in

Hi everyone,

I’m currently an undergraduate student in Data Science, actively working toward becoming a data scientist. So far, I’ve built a foundation with basic machine learning models using libraries like Pandas, NumPy, Matplotlib, Scikit-learn, and some PyTorch. I’ve also explored LLMs by working with pre-trained models through Hugging Face and LangChain. Lately, I’ve been diving into more advanced ML and deep learning concepts, setting up CI/CD pipelines, and learning backend development for ML using FastAPI and Flask.

Despite experimenting with this wide range of tools and technologies, I still find myself unclear about what companies actually expect from data scientists—both at junior and senior levels. What tech stack should I focus on? Which trends and skills are truly valued in the industry?

As a student, it’s hard to get a clear answer on this. Could someone with experience in the field help clarify what companies are really looking for in data scientists today?

Thanks in advance!

<p data-start="78" data-end="94"><strong data-start="78" data-end="94">Hi everyone,</strong></p>
<p data-start="96" data-end="612">I’m currently an undergraduate student in Data Science, actively working toward becoming a data scientist. So far, I’ve built a foundation with basic machine learning models using libraries like Pandas, NumPy, Matplotlib, Scikit-learn, and some PyTorch. I’ve also explored LLMs by working with pre-trained models through Hugging Face and LangChain. Lately, I’ve been diving into more advanced ML and deep learning concepts, setting up CI/CD pipelines, and learning backend development for ML using FastAPI and Flask.</p>
<p data-start="614" data-end="896">Despite experimenting with this wide range of tools and technologies, I still find myself unclear about what companies actually expect from data scientists—both at junior and senior levels. What tech stack should I focus on? Which trends and skills are truly valued in the industry?</p>
<p data-start="898" data-end="1072">As a student, it’s hard to get a clear answer on this. Could someone with experience in the field help clarify what companies are really looking for in data scientists today?</p>
<p data-start="1074" data-end="1092" data-is-last-node="" data-is-only-node="">Thanks in advance!</p>

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Shahir 2 days ago

Tech stack may be wrong starting point here. Companies want answers, so if you can get a model that you could pull an insight like “X increase in metric results in Y% increased revenue.”, that’s one path to take.

You could present that in PowerPoint, PowerBI, in a whitepaper, or as a video presentation. The modality only really matters insofar as you think it’s the correct medium for your audience.

As far as stacks its basically whatever your company uses so Azure, AWS, or GCP generally and the tools w/in those.

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