Broad ask for recommendations for online, heavily quantitative masters programs

Offtopic
#1

I’m looking for recommendations for a masters program.

Here’s some background, which may be too verbose or a little self obsessed, so I’ll “small it” and you can skip if you like:

In the last ten years, through a pragmatic but wandering course, as a software engineer with a passable computer science background, supplemented with lightweight but math non-intensive coursework, I’ve learned the “applied” side of machine learning.

More recently, thanks to resources suggested from this community, I have begun a self study Bayesian statistics, probabilistic programming, calculus and linear algebra.

I’m following my intellectual curiosity, which includes differential equations, symbolic computing, modeling and simulation, economics and finance - of which I’m learning the math prerequisites.

Coming to my ask:

I’d appreciate recommendations for rigorous online masters programs that are heavily quantitative.

Obviously, in-person on-site would be ideal, but I’m a middle aged father, so pulling up stakes isn’t an option. So, I’m constraining my ask with the “online” qualifier but keeping the area of study deliberately wide.

Thanks in advance.

With Gratitude,

Dan

#2

If you want it to lean towards finance (and why wouldn’t you!?), maybe you could check out WorldQuant University https://wqu.org/ which is fully online.

I’m sure if you were looking for something more in the realm of data science (not a specific field like finance), there would be even more options.

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#3

After reading it twice, it is still not clear to me what field we are talking about. Economics, finance, CS?

At least in econ, MA/MSc programs usually have very limited funding, as opposed to PhD.

Do you actually need the diploma? If not, you could just follow online courses and do the work.

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#4

Tamas, yep, that’s a fair reaction. I asked the question without specifying a field.

As before, the context that can be skipped:

There’s a popular science book called, “Range: Why Generalists Triumph in a Specialized World” that introduced me to a term called “late specialization”.

I’ve worked within a range of adjacent specialties within the broader umbrella of software. While doing that, I’ve been an individual contributor, the founder of the company, and an executive.

Despite the premise of the book, neither the private sector nor academia like late specialization. This means that, because of my age and stage of career, what I can do to support my family has narrowed to “CTO”.

But, intellectually, I still am broadly curious, and there are a set of academic areas that make use of the math and statistics I mentioned in my first post. In other words, there are areas of study that are adjacent to one another (at least in one sense) that use similar methods - you can see this represented by the diversity of fields that show up in each individual domain in this Discourse.

When I want to do something seriously, I find it helpful to have a goal. E.g., I wanted to lose some weight so, despite never running before, I trained for and ran a marathon. So, I want to learn more deeply, and having a goal, some structure, and peers would support that. A masters program would do that.

I’d hoped that people, drawing from their own experience, would hear my broad ask, interpret it from their own experience, say, “I loved doing a masters in XYZ from ABC” and that, because so many of the “Domains” here share math and statistics that interest me that something would stand out.

Ending where I began, yep, it was a weird and overly broad ask. I’m frankly grateful that you tried - twice! - to be able to answer it. That’s a great example of why I love this Discourse and this community. I love that when someone asks, “Why is statistics so hard?!?” it gets a dozen thoughtful replies. :slight_smile:

#5

A post-script: I got a DM from someone expressing a similar sentiment and asking how old I am (47).

I responded 1:1 and am referencing the message here because, as evidenced by this message and some other posts from others, I think there’s a group of people who fit between the bachelors-professional and the PhD-academic. Julia seems to be a bit of a beacon for those of use who are trying to “level up” from bachelors-professional, possibly because of its origin a language for scientific computing. People in this scenario will tend to have constraints that people in either other populace won’t have.

My message was an attempt to craft a problem statement within these constraints that would let me move forward more rigorously. I wrote it knowing I’d feel embarrassed, but deciding that a little embarrassment was a worthwhile risk.

Thanks.

1 Like
#6

One thing you will have to “overcome” is that many of these programs will focus heavily on Python. It might even be required at times. Perhaps that won’t be an issue for you though.

#7

That’s no problem at all. I was glad to learn (some of) Bayesian Modeling in the book Statistical Rethinking using R. The language is a means to an end.

#8

In your position, I would could focus on a FOSS project (ie a Julia package, or a set of related packages) used for academic research that you find interesting, read up on the related bibliography, and just contribute code, starting with the minor issues and working your way up to bigger ones.

Code reviews from experts will be educational, and you can ask them for references and interesting research ideas (researchers usually have a ton of ideas that they want to explore, but haven’t for lack of time). I have seen people eventually become coauthors on papers doing this, but I admit that it takes a lot of dedication.

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#9

That’s a neat idea. Thanks, Tamas!