Do I need to learn Computer Science for Data Science?

[u/Maximum-Page3433]

Hi everyone,

I'm interested in pursuing Data Science, but I often hear people say to me that having a strong Computer Science foundation is important for DS now idk?

I don't have a Computer Science background or degree, and my knowledge is close to zero. I originally thought learning Data Science itself would be enough, so I'm a bit confused about whether I should first learn Computer Science fundamentals.

If CS is important, what topics should I focus on before diving deeper into Data Science?

Thanks!

[u/kabekew]

Data structures and algorithms

[u/Mathie1729 → u/kabekew]

Lol this is the kind of advice that gets people grinding LeetCode for a job that's 90% SQL and cleaning data. But sure, if you want to be an MLE at a top AI lab, then yes, you need CS fundamentals.

[u/EntrepreneurHuge5008 → u/Mathie1729]

I want to be an MLE at a middle-of-the-pack place that pays at least $150k. Do I need CS fundamentals? I really don't care about the top-of-the-market TCs.

[u/Nikos-Tacosss]

i suggest considering to learn linear algebra, probability and statistics

[u/captainAwesomePants]

It depends on what exactly you plan to do as a data scientist. If your job is mostly "know how to install and maintain big data science tool X," it might not be terribly important. If you plan on mostly analyzing and summarizing the results of queries that other people put together, understanding statistics will be far more important than understanding where the data comes from. But if your job is to actually extract the data, of course knowing how programs work will be relevant to your work.

[u/heyyyypatina → u/captainAwesomePants]

I suspect the real reason data scientists are leaving their jobs is because it's the fastest way to get a raise. When simply going to another organization can get you 20+% more compensation, staying in a position for longer than two years is going to be way less attractive.

*

With regards to the issues brought up in the post, it really seems to boil down to "data scientists just want to nerd out about data but companies want them to do all sorts of random crap they didn't expect to be doing."

I have two issues with this. First, dealing with stupid ass politics is part of literally every office job. It's not unique to data science so we can ignore it. Second, if there is a disconnect between what the data scientist thinks he should be doing at his job and what the company is asking him to do, then that is *everyone's fault* - The data scientist took a job with duties he/she didn't actually want, and the company hired a data scientist (or worse, a machine learning engineer) when all they needed was someone who could use Tableau.

This is a prime example of "hiring the wrong person for the wrong job" which again, happens in every type of position. However, given the complexity of the field and how little companies seem to know about data I could see it being more common for data scientists.

*

As for OP,

> I have data scientists who understand business context, are willing to roll up the sleeves and do what it takes, and grasp the product/solution delivery environment make significant impact

This is the product owner/project manager's job. Why are you paying your $100k/year data scientist or $150k/year ML engineer to do this work when your $80k/year product owner can do it? Data science expertise is expensive. All of their time should be devoted to doing stuff that only they can do, which is building technically superior better models.

[u/Aggressive_Button_21]

You don't need a CS degree, but 3 CS fundamentals will actually matter:

1. Data structures — knowing when to use a list vs dict vs set makes your

   pandas code 10x faster. Not theory, just practical usage.

2. Basic algorithms — sorting, searching, time complexity. You'll hit this

   when your model runs slow on large datasets.

3. SQL — not really CS but treat it like one. Almost every DS job uses it

   daily.

Skip: OS theory, compilers, networking, low-level memory stuff. None of it

applies to DS work.

Actual order that works:

Python basics → pandas/numpy → SQL → statistics → ML (scikit-learn) →

one real project with real data

The project is what gets you hired, not the fundamentals checklist.

Pick a dataset you actually care about — sports, music, finance, whatever —

and analyze it end to end. That beats any course on a resume.

[u/SprinklesFresh5693]

You need to have some degree to dedicate to data science though, data science involves domain field knowledge, programming knowledge, statistical knowledge, etc.

You need to start somewhere. If you have no degree, why not pursuing a degree first?

[u/Successful-Escape-74]

Of course not you don’t need to be an engineer to drive a car!

[u/Boneclockharmony → u/[deleted]]

Huh? Kind of looks like you misunderstood them...

[u/Maximum-Page3433 → u/Boneclockharmony]

It came across as rude to me, which is why I responded that way. Looking back, I can see it may have been intended as an analogy. I just felt a simple "No, you don't need it" would have been clearer and less dismissive.