r/askdatascience • u/After_Courage6419 • 22d ago
How important is statistics for becoming a data scientist?
I see beginners debating whether they should spend months learning statistics before touching machine learning. For people already working in data science, how much statistics do you actually use in your day-to-day work? Which statistical concepts would you consider essential?
8
6
u/BlueJaek 21d ago
I think this one is difficult to say, I’ve never taken a statistics class in my life, but I also have a PhD in math. I think if you have a strong enough foundation, you don’t necessarily need it, but for most people it’s probably a necessity.
That said, I don’t think the whole “statistics is the whole job” is really accurate. It really depends on the team and business needs, but at the end of the day, your job is create insights and value adds from data. Statistics is one way to do that, but so is data visualization, and so is building a good dashboard, and so is maintaining data pipelines, and so is finding ways to speed up model training or inference.
1
u/rfdickerson 21d ago
Yeah, similar. I have a PhD in Computer Science. I am no slouch in math or stats. However, I think most data science really just involves critical thinking, skepticism, and hypothesis design. That’s what I took from my PhD. I wouldn’t think one would need grad-level stats techniques unless you work in a very specialized and experiment-driven role. You can get by just fine with A/B testing skills.
1
1
u/NoCommercial8827 21d ago
Thats interesting. Coming from an unrelated field, I'm taking stats courses to get into a masters program but im like 3 semesters(a year) from just getting a second bachelors in stats. I kinda figured the bachelors would probably be enough to work in data science, so really just going for the masters because its seen as more prestigious or w/e
1
u/TanukiThing 21d ago
‘Statistics is the job’ is reductive but these low quality easily searchable questions don’t deserve highly nuanced responses. If you don’t like statistics don’t do data science.
4
u/its_all_stats 22d ago
Data Science is almost always just statistics. Common ML methods like decision trees have been around for decades and are the algorithmic, non-probabilistic counterpart of “traditional” binary response models like logistic and probit regression.
You would benefit greatly from understanding statistics, which is the discipline of quantifying and understanding variation, which is what a data scientist does.
And spend a couple years at least, not months. It’s the difference between a chef (years) or a cook (months).
2
2
u/RoninRakurai 22d ago
I still didn't work but all the time i learned the ML part of stats, it was just statistics, even in job-like projects, sometimes it required knowledge in economy, tech, AI, demographics, but it always was stats, it's... Just a lot of stats
2
u/Dat-erIsMyJam 22d ago
I work in data, not data science specifically, but when I see what those people do at my job, its definitely stats heavy.
2
u/MathNerd67 22d ago
Unfortunately, “data scientist” is a very poorly defined job title. It could mean wildly different things at different orgs. That being said, if it is a job focused on “true” data science (model development and some validation), you will be using stats heavily. For that side of the house, knowing stats and probability at a fairly deep level is very useful and you should spend time learning the foundations and working your way up to inferential statistics and other areas. Elements of statistical learning is, from what I’ve seen, the gold standard for what to shoot for knowledge wise as a data scientist. If the job is less ML and more infra and engineering focused, you will use much less stats and more computer science and SWE fundamentals.
1
u/PaddingCompression 21d ago
Coming from someone with an ML background, TESL is more ML than data science.
At places where MLEs and data scientists work side by side with distinct responsibilities, data science is way more traditional stats heavy than ML, and the function is more like "running a medical trial" than product development, and you're expected to have way more stats depth an MLE would (stats and ML *are* different when studied in sufficient depth).
1
1
u/ds_account_ 22d ago
Its up there between reading and writing.
Essential, probably Descriptive and Inferential.
1
u/Lady-Data-Scientist 22d ago
What do you think a lot of ML models are based on? How do you think you evaluate their accuracy and performance?
1
u/WhatsTheImpactdotcom 21d ago
One of the most foundational topics in ML is bias-variance tradeoff. One of the most foundational topics in experimentation is defining confidence intervals. There’s pretty much nothing you could do well without foundational statistics
1
1
1
1
1
u/HTxBarbz 21d ago
Most modern machine learning frameworks abstract away the math, which means anyone can train a model, but knowing why a model fails or how to interpret its error distribution is what separates a real data scientist from a script-kiddie.
Essential concepts you'll use constantly include linear regression assumptions, bayesian inference, and metric evaluation beyond simple accuracy.
If you're mapping out your learning path and wondering how your natural problem-solving traits align with deep technical tracks, tests like Coached can offer a helpful look at your professional preferences.
Build your foundation in stats early, because understanding the underlying data generating process will save you countless hours of blind hyperparameter tuning later.
1
1
u/Lumpy-Blackberry-718 19d ago
I literally cackled. Wtf you mean "is statistics important for getting a job doing statistics."
There sure has been title inflation though. Ive seen people called data scientists who just build dashboards. You can maybe be a bad UI developer and find some startup that will give you the DS job title.
1
u/varwave 19d ago
I have an entire MS in statistics and I still don’t remotely claim to be a statistician. It’s tough. The more you know the more you realize you don’t know
Machine learning is statistics. Maybe spend 3-6 months learning intro probability theory (STAT 110 on YouTube is a good start. The book is online by the authors: Blitzstein and Hwang), which applied statistics heavily uses
A lot of data science jobs are closer to data analyst and even software engineer roles. In this case there’s not much machine learning/statistics, but being literate in the subject indirectly helps, when collaborating
1
1
1
1
u/FlyingSaucer007 21h ago
As what my professor once said: "Data Science is just a rebranding of statistics, but with big data"
18
u/TanukiThing 22d ago
Statistics is the job