![]() I'm glad to hear that the Analytics program has a more dedicated focus on practical matters. 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. 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. And to all who make this greatly empowering service possible: thanks, and keep up the good work.įair enough, and I should have been a bit clearer in my original post. I say, more power to GT's authors, curators, and administrators who made this possible. And when done right and priced-right (as I believe GT does), I have nothing but kudos to offer in return. Few of us pros can return to campuses, even part-time. ![]() 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. 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. 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. Few computing pros submit proofs among their deliverables. In general, while theory has great value, it's more as a stepping stone to higher study than as an end unto itself. But if the role of 'higher education' is to be a practical one (as engineering programs have always been), it only makes sense for schools to ask industry what it needs and then serve those ends, first and foremost. I have an example I'd like to write up, but it's a bit long for a footnote.Ĭertainly with the rise of sham/for-profit universities, sales pitches promoting 'practicality' now launch red flags, and deservedly so. In particular, my mindset shifted to one of models of computation and decomposition of problems into subproblems for which the simplest model could apply. : For undergrad that dubious honor has to go to Olin Shivers, not only because of his eclectic teaching style, but also because his class completely altered the way I think about problems in computer science. Correctness is near and dear to my heart, but performance is right there with it :) The material from the couple compilers classes I took on a whim has been a huge boon when talking about software correctness. ![]() : In particular, the material covered in my graduate systems classes has been invaluable for not reinventing the wheel for the thousandth time. It was also probably the highlight of my graduate career. In addition to high quality, pre-prepared lectures peppered with entertaining anecdotes, the had high quality projects that worked with pratcial tooling. Charles Isbell's Intro ML class was a significant exception to the pattern I described above. It's interesting that you brought up machine learning. In the years subsequent to that, the grounding from those classes has given me starting points for deep dives into problems I encountered at work. I was planning on pursuing a PhD when I started into my MS, so this format worked quite well for me at the time. This naturally leads to a format where the semester can effectively be described as a long reading list of papers and lectures to spur discussion on the content of the paper. That said, with a couple of notable exceptions, the graduate classes are there for PhD students as first and second year background material so they have some starting points for their research. While I have many complaints about the school, the quality of the classes is not one of them, for either the undergrad or grad programs. I did both my BSCS and MSCS at Georgia Tech.
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