In general, while theory has great value, it's more as a stepping stone to higher study than as an end unto itself. Few computing pros submit proofs among their deliverables. And devising the theta bound on a function or resolving the terms of a CSP simply don't deliver much value when working outside PhD-level R&D labs and writing peer-reviewed papers.

I believe there's a great deal of value in applied non-PhD track academic programs like GT's online discount offerings, especially in serving professionals and employers. I also believe it's high time that universities clued in to the unmet need that most of us post-academics face toward helping us continuously re-educate ourselves as we progress through our careers. Few of us pros can return to campuses, even part-time. Distance learning meets a crying need. And when done right and priced-right (as I believe GT does), I have nothing but kudos to offer in return. I say, more power to GT's authors, curators, and administrators who made this possible. And to all who make this greatly empowering service possible: thanks, and keep up the good work.

If you want to play around with theoretical computer science, get your PhD. College educations are too expensive to not be imminently practical.

It's a waste of time to teach industry tools at a university. It's much more valuable to be taught fundamentals. Know your fundamentals well and any new tech will be much easier to learn. It's long-term thinking - put in the investment to make sure you can change skillsets in the future.

All the things you mentioned tend to be ephemeral and change a lot within a few years. Look at the git monoculture that's sprung up in the last 5 years for example - 10 years ago it might have been reasonable to teach SVN.

You have to do programming assignments anyway. Why wouldn't you require students to learn and use the latest source code control tools while they're doing their development?

Teach students to write tests, use source code control, utilize continuous integration, etc.

Although the specific tools, languages, and approaches will evolve in the coming years - none of the above are going away soon.

Time spent making undergraduates use git is time that could be spent teaching them long-term, fundamental skills.

there's no better way to learn something difficult than to learn it when you're young

Hmm, define 'young'. I'm in my late 20s and I find it easier to learn new things more than ever - including things I failed to learn in my teens and early 20s. Maybe I'm just a late bloomer, and it took me a while to "learn how to learn". But maybe I'm still young in the eyes of someone who has been using vi for 30 years (:

Testing ect is usually covered in all intro classes (assert libraries) or industry type testing like JUnit by a software engineering elective typically taught in Java

Just because one of gits key abstractions is based on a kind of graph, I don't think it follows that knowing graph theory means you know git. I mean LISP is based on a graph structure as well but plenty of people find that confusing.

In my experience with the MSCS program (nearly ten years ago at this point) the core required classes were mostly well structured and would serve people well continuing onto a PhD or growing their skill set for industry. The core constituted a relatively small chunk of the overall credits required, though, and the elective courses tended to be more along the lines of what I described.

I'm glad to hear that the Analytics program has a more dedicated focus on practical matters. It might be interesting to produce a series of similar (but narrower) curricula that amount to curated collections of CS classes making up degrees in Machine Learning, Systems Programming, etc.

I personally really enjoyed my dartboard-oriented approach to class registration. I learned more than I've never needed to know about approximation algorithms, cryptographic theory, and compilers. Even if much of what I learned there hasn't proven itself directly useful yet, I really enjoyed learning it for learning's sake, and I think I'd have had a hard time picking up some of the gems I pulled out of that since. I also still have a hobby of proving problems NP-complete on demand as a bit of a parlor trick (within the limited scope of problems for which you can apply the small handful of patterns I've burned into my brain over the years :).

I did, however, find that my undergraduate university had a great program for people with nearly complete degrees who had been away for a few years.

I'll be finishing undergrad this May and am now looking at grad schools. Feel free to contact me if you want to chat about this because it's been surprisingly hard to find info or advice in our situation.

Who can apply to the OMS CS degree program? Admission into the OMS CS program will require a Bachelor of Science degree in computer science from an accredited institution, or a related Bachelor of Science degree with a possible need to take and pass remedial courses. Georgia Tech will handle the degree admissions process. For more information please visit the Georgia Tech program page.

Mostly you use python numpy and scipy to analyze a large time series data set (stock market) to predict pricing while having a low correlation to the overall market movement.

I had some success and won their 6 month contest, but I still feel like a bit of a hack. I'd like to move into the financial quantitative analysis industry.

Would you say this GT program would be a good stepping stone?

What is the best way to get in touch with you and get the syllabus material for the courses?

I'm at rememberlenny at gmail.