GT's MS in Analytics degree is actually designed specifically for people who are going to go out and work in the analytics field -- it's not a pre-PhD degree, and our courses are targeted primarily at people who want to learn and apply analytics. We have an industry advisory board that helps us target course and program content, and we're constantly working to make sure our coursework is focused to the right cohort. We even have a required applied analytics practicum (both for on-campus and online students) where our students work on analytics projects for a wide range of companies and organizations.

Perhaps other degrees are different, but the MS Analytics is a very practice-focused degree.

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.

It's amazing how you don't think of someone for almost 30 years, but you read their name in a comment on HN and memories come flooding in. What do those neurons do while they're waiting to be used again?

I also did BS and MS at GT, and while I generally share your experiences there were 3 or 4 truly disappointing classes during my MS. They didn't ruin my overall experience, but I can see how someone could happen to have more experiences like those and fewer positive ones and come aware with a very different perception of course quality.

My overall opinion of GT is mixed, but rigor or the courses is not one of my top critiques.

In the year I did it, the class was structured as follows:

At the beginning of the semester, you'd pick two datasets.

Every two weeks, you'd apply two or so algorithms that were being covered at the time (maybe k-means and SVD, or a NN and SVM) to your chosen data sets. There would be a set of variations that you were supposed to apply to each algorithm. Typically you'd normalize or clean the data in some way. Perhaps you'd filter outliers, etc...

The result would be a set of experiments to run (2 datasets) x (2 algorithms) x (2^3 variations per algorithm). You would compile the results into a (10 page max) paper, with analysis about how the dimensions differed.

It was up to the student to figure out how to actually implement this pipeline (I used sqlite + numpy/scipy/scikitlearn, many used Matlab).

On paper, this sounds like a great class - what a wonderful way to learn about how different approaches relate to each other, and how crucial the process of preparing data is to the effectiveness of the algorithm. In practice, however, this did not happen for most students I knew.

These students spent most of their time finding implementations of the algorithms and hacking at them to actually run all the experiments. They then rushed through gluing the results together through some semblance of analysis. Alumni of the class I knew said the same thing about their experience.

This analysis was read by TA's. There were I think 3 of them for about 100 students. We wouldn't get the papers back for weeks (long past we moved on to new material). When we got our papers back there was very little feedback of the content - mostly it was noted that we submitted the work on time, and had successfully performed all the experiments required.

I agree that Isbell is a joy to listen to - he is charismatic, entertaining, and I too enjoyed his anecdotes. However, I felt like you would only get something out of his lectures if you already knew what you were talking about.

When I think about the quality of the class, I think about how responsive the class is to the individual needs and progress of the student.

If you say that it's up to the student what they get out of the class, and your bar for a good class is that the content is arranged in a nice manner, then here you go https://pe.gatech.edu/sites/pe.gatech.edu/files/agendas/CS-4... ... any self-directed student can grab Mitchell, and do the weekly assignments I describe above - all for free and in the comfort of their own home.

This is especially true of term project courses, where the final portion of the project to which you devote the most time and creativity is also the part for which you're likely to receive the least feedback.

I disagree (having taken the course as an undergraduate and it being my first major exposure to machine learning). Certainly if all you do is attend the lectures, you're going to miss some background knowledge, but that is true of most (if not all) university courses. You're supposed to devote 2-3 hours of outside work for each hour of lecture. Meaning 6-9 hours of studying per week outside of those lectures.

Some of this is doing the projects, although some of it is personal investigation.

There are failings of his course (one of the biggest at this point is that it doesn't do any work with the state of the art now), but I think that the fact that his course caters toward people who are self-driven is not a failing.

The best way to look at what the goal of the course is is by looking at his exams. If they weren't different than you took them, they were intentionally too difficult for the allotted time, leading to low averages and incomplete work by the majority of students.

However, the course allows motivated students to make connections between concepts, with the help of the professor and the coursework. Having someone "leading you" down the right path is very helpful, much moreso than a textbook alone.

I really do think that there is one exam question that sums up Isbell's course perfectly: its the one where you are asked to compare and contrast 4-5 aspects of 4 randomized optimization algorithms (RHC, GA, SA, and MIMIC) and explain situations where you'd use each and why.

The course's goal is to lead to a strong intuition for the algorithms covered (sadly at the partial expense of a theoretical understanding), not everyone puts in the work to develop that understanding, but that's not a failure of the course, necessarily.

You can find the syllabus for Isbell's class and follow along. You can do the readings and programming investigations. If you like lectures, you can find many full courses on YouTube (I found caltech's lectures https://www.youtube.com/watch?v=eHsErlPJWUU to be the best at presenting SVM's out there, although this was probably my third attempt at understanding them so maybe the other resources rubbed off.. they also skim over the quadratic programming detail but I get that this may be beyond the detail that many people desire in an intro class).

If you have to teach the material to yourself, how is your experience improved by being in the class?

To be fair, most of Isbell's course (lectures) is also available on Udacity.

>If you have to teach the material to yourself, how is your experience improved by being in the class?

There are a couple advantages. One of the most obvious is the lower latency of responses when you have confusion or misunderstanding. In a lecture, you can ask a question and get an answer almost immediately. This is most useful (imo) with algorithms and mathematical concepts, because you can ask, and lecturers are often quick to provide insight, into the interrelationships between algorithms (both in Machine learning and in a more theoretical sense like computability). There are topics that come up a lot, and being able to have instant feedback on those connections allows you to spend less time misunderstanding than not.

That alone is a fairly weak justification, I think the stronger one is feedback in general. Watching lectures only gets you so far. With implementation of algorithms, often your feedback is testable correctness (although my experience in DS&A suggests that most people are capable of constructing incredibly incorrect models for things that perform well on some input, and even on decent autograders), but with things like machine learning algs and intuition about those algorithms, you can't get that. So the feedback that yes, your understanding is correct (even if that feedback is slow) is invaluable. In that regard I think online courses and MOOCs can be good, but MOOCs that don't provide feedback aren't as valuable. I've attended a lot of lectures, and I've ignored a lot of lectures. Listening to someone say something does not mean one has learned it.

I'd also note that, if I recall, the way that Isbell approaches teaching the material, vs. the way the textbook does are very different. Textbooks are (often) references. They provide information on what something is and how it works theoretically, but very often lecturers are able to provide the kinds of things that aren't (and shouldn't?) be in textbooks.

If I'm reading a textbook, its very likely that I want to know how to implement an algorithm, so I care that the algorithm for simulated annealing says that you jump with probability e^(D/T) > Rand[0,1]. Whereas in a lecture, I'm likely much more interested in the idea that simulated annealing is conceptually very similar to throwing a ping-pong ball into a large complex, convex plastic surface and seeing where it lands.

I don't agree that feedback during lecture is valuable or low-latency as you say - not with 100 students attending. It might work to ask a clarifying question here and there, but again - you're only in a position to take advantage of that if you're already comfortable with the material and are generally keeping up.

Books are different than lectures, sure, but I don't think there's much difference between attending a lecture with 100 students, or watching one online. Indeed many people claim the online way is better, since you can rewind and skip around, pause and lookup references, etc...

I'd agree that Tech has too few TAs for too many students, generally, for its graduate courses, but I don't know that other schools do a better job. A brief survey of the folks around my desk elicited howls of laughter at the notion of useful or accessible TAs in grad school.

> I agree that Isbell is a joy to listen to - he is charismatic, entertaining, and I too enjoyed his anecdotes. However, I felt like you would only get something out of his lectures if you already knew what you were talking about.

I think this assertion is, at best, too strong. A better assertion might be that his lectures depended on coming in with sufficient background.

As I said, I came into the course with no experience with machine learning at all. On the other hand, I did have a fairly strong theoretical computer science, stats, and linear algebra background. I will admit that may have made me blind to things he was simply assuming with respect to educational background that were not actually safe to assume. That said, I still refer back to his primer on information theory (http://www.cc.gatech.edu/~isbell/tutorials/InfoTheory.fm.pdf) when discussing work relying on it, so he certainly made some effort to fill in gaps as he discovered they were common.

> When I think about the quality of the class, I think about how responsive the class is to the individual needs and progress of the student.

For a graduate level course I feel a class clears this bar when it accurately and thoroughly documents the prerequisites. Now, I'm not saying Charles's class necessarily does this. As I said, I came in with a pretty strong background in what turned out to be more than sufficient, but with that background I personally felt his lectures were quite tractable, even assuming complete ignorance of ML itself.

Ironically, this sounds quite a lot like much of industry.