a degree in computer science is really inefficient if you want to be a data scientist. imagine a 10 class CIS masters: with graduation restrictions you might be able to take 3 classes that directly relate to data science. good luck in your job interviews if you took them first, as you spent 20 hours a week on homework to fill requirements you will never use.

now take a statistics major, every class is relevant, and you can still take machine learning in your electives. win win.

I came to this conclusion after I noticed more of my classmates in the mba program (wharton) as data scientists than people in computer science who took machine learning. in fact, _all_ of the CIS majors in machine learning who really wanted to be a data scientist ended up as engineers.

so then I started doing a small search on linkedin, only looking at the big tech company data scientists. selection biases aside, out of 12 profiles: 5 statistics majors, 5 business, 1 biophysics, 1 IT major.

I have also done some looking into interview questions via glass door, and you get grilled on statistics questions. this matches my one interview with uber in 2016. I only got asked 2 ML questions: what is random about a random forest, and in KNN, what happens to bias & variance as K goes to 1

if you want to be a data scientist, you need to learn stats really well or getting past the interview process is going to be very difficult.

Statistics and machine learning have a huge amount of overlap. Almost seems silly we separate the fields.

Well yes. Before the trendy buzzword, machine learning was known simply as predictive statistics.

No idea why this was downvoted; I find this to be an accurate description. The difference between stats and ML is mostly one of terminology and perspective. I studied math in college and grad school, and there were several moments in Andrew Ng's online lectures where I thought "oh, I know this, but we didn't call it that, and I had no idea it was considered ML."

reminds me of the whole Bayesian perspective of ML

Or simply statistical modelling. I think there are methodological differences though. To me ML seems like a massive p-value fishing operation.

Do you mind elaborating? I can't think of any methods that explicitly (or implicitly) use p-values. The only place in the industry I have seen p-values used is AB-testing, but most seem to be trying to move to a multi-armed bandit, bayesian methodology.

Statistics is used heavily in machine learning.

Questions look answerable for someone taking 2 courses in machine learning. One being the introduction.

Machine learning systems that perform a job and make money are still overwhelmingly stats-based, and the knowledge required to understand, tune, and optimize any ml system, regardless of design, are based on statistics and always will be.

I'm not sure I wholly agree there. There's an awful lot of real work being done that makes real money based on deep learning, and deep learning comes from the CS "it works, what's the problem" side of the field as opposed to the statistics "I want formally grounded theory for everything" side.

One of the big pushes in Bayesian statistics recently has been to try to figure what the hell all these neural nets are actually doing. It's certainly not the case that the stats have been in the driving seat there.

Classical AI is applied, layered logic.

Modern ML is applied, layered statistics.

season to taste.

I have the impression that at big universities maths is always the #1 topic in CS/ML. So its no surprise their graduates ask the same riddles as their profs.

I personally know a few ML professors at my university who are not taking any CS grads as pHDs. They only want people with maths degrees.

A lot of these CS professors are themselves maths grads.

CS is discrete maths, which is not quite the same as statistics.

People have been doing statistics for decades, which is mostly all that machine learning is. Coming up with a fancy new buzz word for stuff that has been for decades, doesn't make something "elite".

You know, statistics is a branch of mathematics, and mathematical education itself can be considered "elite", if for no other reason then for the irrational fear it induces in most of people.

Most of high school graduates decides to pursue more "practical" topics like CS, leaving mathematics to those chosen few who dared to study it :)