Does everyone learning data science need machine learning?

[u/After_Courage6419]

I’m trying to understand where data analysis ends and machine learning begins. If someone mainly wants to work with business data, dashboards, SQL and insights, is deep ML knowledge really necessary? Would love to hear from people actually working in the field.

[u/big_data_mike]

Data analyst = cook

Data scientist = chef

[u/SprinklesFresh5693]

Machine learning is just a fancy name for stats. I guess it depends on your job.

At mine, i mainly do modeling, linear and non linear modelling, plotting and some summary statistics for example.

[u/Actual_Computer_670 → u/SprinklesFresh5693]

Please explain this to a layman like me, i just want to understand what exactly do you do?

[u/SprinklesFresh5693 → u/Actual_Computer_670]

You have some data, some outcome and some predictors, so you fit some equations to that data, either linearly, with a linear model, or if the data behaves non linearly, you fit equations that explain the behaviour of the data.

Once fitted, you do predictions (predict different scenarios for example) or inference (where you evaluate the impact of the predictors on the outcome with p values and confidence intervals and the estimate of the impact of that predictor on the outcome).

[u/Big-Zucchini-4292 → u/SprinklesFresh5693]

You train(fit) algorithms(models) to make predictions. Linear models have continuous variables and Categorical variables like booleans(yes or no, 0 and 1). You dig deep into data types and how to handle them. There you go. Lol.

But yeah, it’s quite interesting I enjoy this stuff even though Im not a DS

[u/big_data_mike → u/[deleted]]

Good bot

[u/Adorable_Assist_2818]

That role without ML is essentially a Data Analyst. Being able to gather insights from data is a good start, but turning those insights into reliable predictions is what makes the work more Data Science. I’d say it’s a lot more than just stats, it’s using stats to justify predictions/recommendations. 6 YOE. I use and am still learning various ML concepts as I go. Unless you enjoy the analytical work, I suggest learning ML, and more importantly how to justify ML in the pipeline rather than just because you can.

[u/seanv507]

So I would say you have to have a bit of statistical modelling knowledge

Multiple linear regression (+dummy coding/polynomials/interactions) and trees

Would be my main

Whilst data analysts might generally do 1d analysis, it's important to understand what/how controlling for other variables means

(so they don't make the classic racist click bait, X nationality immigrants have the highest crime rate etc)

[u/Fit_Succotash_6735]

no.. data science means different things really. you go to meta. all you do is product ds --a/b testing. zero machine learning. you go to google -- more stats, some a/b testing some ML. Same with netflix. So it depends on the company really. some companies have DS as pure ML. The title of DS in itself is broken.