As someone who completed a (on-campus) CS masters at GT, I really wish I didn't. The classes were of very poor quality - it was clear that they were a low priority for most faculty. Andrew Ng's Coursera class on Machine Learning was the pedagogical highlight of my time at GT, and I did it on my own initiative (and it's free).
I know people have many reasons to get a Masters. If your goal is to take some higher-level classes, you can do better than GT. If you are self-motivated enough to do an online degree, you can do it for free. Aside from free offerings from sites like Coursera, you can find whole courses up from many institutions - with syllabi, lecture slides, homework assignments, etc.
If you're planning to do it for the salary, in my experience the degree ended up being worth a $95K to $115K bump in starting salary. Compare this with the 2 years of industry salary that I would have received, and the 2 years of experience (and raises that come with that). I know I wasn't paid better than the folks who had been with the company for 2 years more than me.
If you're thinking about it for the sake of your resume, I do hiring screening / interviews now (for Data Science and Software Engineering positions) - and I really don't care if the applicant has an MS or not (or what classes they may have taken). Most folks I know that do hiring think similarly. My main signal from your resume is projects you've been on and how you contributed.
From my point of view, this program is a losing proposition for any potential student.
I just graduated from the OMSCS (online master of science in computer science) program, and I found it a wholly worthwhile experience. It was challenging, informative, and for the most part well-run. Software Analysis and Test in particular was a real eye-opener. And while Computability, Complexity, and Algorithms was a hideous death march of terror, the material they covered was some of the most interesting I've ever experienced.
Yes, you can study the same material on your own, but you won't earn a degree from it. Now that I've got the degree, I'm in much better shape to pursue further learning on my own.
Note, however, that I didn't do this to improve my resume, go fishing for a new job, or try to get a raise. With tuition reimbursement from my company I only spent $3500 over 2 1/2 years to earn a full-fledged master's degree.
Based on the above, I can't agree that it's a losing proposition.
"Now that I've got the degree, I'm in much better shape to pursue further learning on my own."
Could you explain a bit more what do you mean with this statement? Is it that you feel better prepared to study advanced topics (like advanced ML/Data Science) or was the degree a requirement for something else you wanted to pursue?
I'm curious about what other "doors" having this degree opens, other than the bump in salary mentioned by others.
I did both my BSCS and MSCS at Georgia Tech. 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.
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. 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. I was planning on pursuing a PhD when I started into my MS, so this format worked quite well for me at the time. 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[0].
It's interesting that you brought up machine learning. Charles Isbell's Intro ML class was a significant exception to the pattern I described above. In addition to high quality, pre-prepared lectures peppered with entertaining anecdotes, the had high quality projects that worked with pratcial tooling. It was also probably the highlight of my graduate career[1].
[0]: In particular, the material covered in my graduate systems classes has been invaluable for not reinventing the wheel for the thousandth time. The material from the couple compilers classes I took on a whim has been a huge boon when talking about software correctness. I work on the hypervisor underneath GCE. Correctness is near and dear to my heart, but performance is right there with it :)
[1]: 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. In particular, my mindset shifted to one of models of computation and decomposition of problems into subproblems for which the simplest model could apply. I have an example I'd like to write up, but it's a bit long for a footnote.
Hi! I'm the director of Georgia Tech's MS in Analytics program (both on-campus and online).
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.
Charles Isbell is still at GT? Holy cow. I think he was the teaching assistant when I was taking VAX assembly back in the late 80's when I was there. Seemed like a nice guy.
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 did both my BSCS and MSCS at Georgia Tech. 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 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.
I took Isbell's class as well, and perhaps here we can share our respective experiences.
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.
I also graduated with a MSCS from GT. While I agree that a Masters degree is not a good signal for a job candidate, having Georgia Tech as your last institution of study instead of your potentially "lower" undergraduate program is.
The question remains is if an online degree has the same credibility. Looking back at my time at GT, I cannot see how operating solo, without the constant feedback from your peers and faculty, is as good. There is more than just what is in the study material. The other question is if the entrance requirements are still as stringent.
From my perspective, unfortunately, Georgia Tech has really diluted the value of their masters in cs degree. They have become a sort of immigration visa-mill with very many India undergrad -> Georgia Tech Masters of very questionable skill level.
I'm sorry you didn't like the on-campus experience at Georgia Tech -- but the online program sounds like a clear win. For less than $10,000 tuition, you could've gotten a top-10 degree and your $20,000-per-year salary increase... all while still working, so you would've also accumulated raises and experience while finishing the degree.
The question would be whether the time spent on this program compares to what one would be able to accomplish on their own, and whether the difference is worth the price tag.
From my experiences with GT, I am apprehensive about the quality of content - I assume it's coming from the same departments and professors that I had experience with.
From other comments here it seems that the approach this program took was quite different from the one employed on campus, so that apprehension may be unfounded - that is, the online offerings may be of higher quality than what students on campus receive. Still, I felt like I needed to post something to warn people that the branding of GT does not in-and-of-itself mean that the content will be of high quality. Students considering the program should try and find some way to evaluate this - are there sample classes or lectures posted online? Perhaps ask someone you trust in the industry to take a look and give you their feedback.
Still, I think students who are self-motivated should consider what they would be able to accomplish if they took some time to organize a study program for themselves. There are many free high-quality resources out there that could be used for effective self-directed study.
Students who are less confident about doing it on their own should be asking themselves what sort of support they expect to be getting from the program. Certainly there are many advantages in having things curated for you, as well as having access to discussion boards with other students going through the same material. Aside from that, many students (unfortunately, I think), need the external schedule and commitment - and for them, merely having an exam deadline, or the $10K investment looming in the background may be the thing needed to get through the material. Those students, too, should be realistic about the investment they are making and what they hope to get out of it.
Does it remain a top-10 degree if thousands of people take the course every year? Genuine question. I work in education, and we struggle with this selectivity vs. access question ourselves.
I agree, and the $10k tuition would be likely covered by your employer. Some of the more expensive online MS programs still have significant out of pocket expenses if they are $40k or so
>Andrew Ng's Coursera class on Machine Learning was the pedagogical highlight of my time at GT, and I did it on my own initiative (and it's free).
This is something I wanted to highlight from your post. I don't think this is surprising, nor do I think it is reasonable to state that a course (or a degree program) is poor quality because it didn't meet the standards of Ng's ML course. That is an exceedingly high bar.
Its something I noticed, because I am a recent grad, so while I was in my mid-level courses, and had recently taken Tech's intro CS course, I was able to watch (Harvard's) CS50 and other courses. But on the other hand, I've seen some very bad online courses. The successful and large online courses are successful and large specifically because they are head and shoulders better than the rest. And there are a lot of decent online courses, so to measure against what are some of the absolute best online courses is to measure against courses that have more resources, more planning, and more feedback than most.
(as an aside, they also have more incentive to be good, but that's a bit tangential to the point that they also have more opportunity to be good).
You type in "Machine Learning" on coursera and you get over 1000 results (not all of which are relevant, but assuming even 10% are), its little wonder that one or two are going to be better than the even the best courses that you'll take during a bachelors or masters, because Coursera offers more Machine Learning courses than most people will take in their Bachelors or Masters.
Combine that with these courses coming prefiltered (you've heard of the Stanford Course, but what about "Applied Text Mining in Python" from UMichigan, which for all I know might be great, but it doesn't come with the hundreds of recommendations that the Ng course does, so I don't know that it will be great) and you have a really great recipe for a bias against the in person courses.
This is interesting. I had heard good things about the program, so I'd love to hear more about what ultimately went wrong.
What track were you on? Do you think that had anything to do with it? What did your peers think about the program?
A $95 to $115k bump sounds pretty darn good. Did you already have a CS undergrad degree? Where from? Sorry for all the questions, feel free to share as little or as much as you're willing.
I was on the Machine Learning track. Already had a CS degree from a prestigious school. I took classes primarily in the ML concentration, although I had to take some courses in theoretical cs and they were similar.
I'd say my peers generally shared my opinion (classes not being very good). Many of them were trying to get into PhD programs so they were focusing on finding research opportunities and didn't care about the quality of classes much. Some struggled but blamed themselves for this rather than the class. (I'd say this was very common amongst the undergrads too).
Of the PhD students I knew, most were discouraged from taking classes altogether, since it took away time from research. This was true even in the first year of their program. The general attitude from that side was that classes were a waste of time.
Many higher-level classes were run as mini-research projects. You'd get some content, then the rest of the class would be forming teams, proposing project ideas, implementing, writing up results and having 'mini conferences'. I think faculty liked this since it was a good way to try out research ideas, recruit potential PhD students, and give their current students extra time to work on their research.
Nothing wrong with that format, of course, but the actual coverage of content was typically superficial. If you weren't already familiar with the area, you had to figure it out on your own as you went along. Also, this is not a class format that translates to online very well.
What you say is often true, however there are a number of places that will shitcan your resume if it doesn't have an advanced degree on it. For example SpaceX, but there are others, often in technical fields.
Also, hang in there. The degree may not have helped your current job as much as you'd like, but it may help more at a future company. Sadly we often need to change jobs to get a real salary bump.
I think you're looking at it in both a false comparison (opportunity costs for online are very different than your on campus experience; not to mention the explicit costs) and backwards (as to who is looking to get this degree).
The margins on adding a MS CS (or analytics) is less for a CS grad, but at 10k a degree and the flexibility of taking online they are huge for non-CS majors looking to pivot. 10k to bump your salary up 20k (while working and getting experience + raises) plus the knowledge add pays for itself.
Im biased on account of being in the program (supplementing my Electrical Engineering degree due to a change in career and life paths), and I know a lot more about graph theory, formal algorithms, and high efficiency computing than I did a year ago. These are things that help improve both your portfolio of rigorous projects (Im currently working to port over all of my high performance computing assignments to Rust) and assortment of tools for the ever annoying CS interview.
I'm enrolled in the OMSCS program currently (nearly graduated) while working full time. I started the program because I was already taking the online courses with Coursera, edX, etc, and it felt like a natural fit to get a degree while I continued to do so.
I didn't have a Computer Science undergrad degree, so perhaps I have a different outlook on this than you did. I'm currently working as a software engineer, so I'm also not forgoing earning a living by taking the time off from school.
In other words, while I can understand why you feel disappointed by your educational experience, there are other lenses through which this program makes sense. I feel good about having gone through it so far and I'm looking forward to finishing.
It sounds like you did the on-campus program at GT, so you took time off work to complete it. It's hard to argue with the fact that losing the wages to do that is a lot of missed opportunity.
However.... I think majority of people that take OMSCS (I am in my 2nd semester) and this new OMSA program do so part time while still maintaining their fulltime jobs and families.
I'm enjoying the MSCS program online so far and am also doing the ML specialization. I work full-time as a software engineer and just do the coursework part-time. My experience so far is that the assignments and learning are as rigorous as you want them to be.
I'm getting exactly what I'm looking for out of it (a somewhat structured environment to learn in), so for me I would say this isn't a losing proposition.
Really? Pretty much all feedback for OMSCS has been very positive. It sounds like you're bitter about something, or had a particularly bad experience. Can you provide more details about classes you thought were poor and what was poor about them?
I am bitter about having spent time, money and emotional energy on something that I felt I got no value out of, and I want to warn people thinking about doing the same.
> From my point of view, this program is a losing proposition for any potential student.
Although I agree with your sentiment (formal education has a lot of flaws), a full-fledged degree potentially solves other problems than just improving your resume to get a better/higher paying job for engineers.
Family pressure, lack of confidence that comes with not having a formal CS undergraduate degree, or motivation and structure that comes from paying for a formal program could all contribute to someone choosing this path. A $10k, online masters program from GT seems like a good option for some people.
I haven't gone through other programs, so I can't say from personal experience.
There are no programs that have stood out to me during hiring to predict the quality of the candidate.
If you're a student looking at programs, I would prioritize the amount and quality of individual attention you stand to receive. A self-directed student can do well anywhere (including on their own). If you're not so stubborn/resilient, having a good community and mentorship to help you overcome difficult times is key.
For what it's worth, GA Tech is an amazing school, but I do think they are far more known for their traditional graduate engineering programs (aero, mechanical, electrical, etc.) than CS.
It is kind of funny in a post about a program which costs under $10k to have the top rated comment complain about a similar one _only_ adding $20k/yr of value.
I have both a B.S. and M.S. in Computer Science. I didn't get my Masters because I wanted a higher pay grade (although the company I was working for at the time did reimburse a large amount of it), it was to get out of industry.
From my first job, I realized life in a cube wasn't for me. I really wanted to be in front of a classroom. I realize there are problems with academia. I know you have the same squabbles and competition you have in the corporate world, and seeking certain grants to keep yourself afloat can cut into the research you actually want to do.
Still, I really wanted to teach. I've seen so many professors who only work one or two jobs, or go straight from BS -> MS -> PhD with very little industry experience. I wanted to be a different type of professor with plenty of real work experience to drawn on and teach from.
Grades don't matter. I've found that's very true for industry. Having a GPA on your CV doesn't really mean anything and most people leave it off. However, it has a huge impact on getting into degree programs.
I only had a 2.5 undergrad and even though I got a 3.2 in grad school, it wasn't enough for most programs I looked at. I attempted and failed to get into 8 schools back in 2009 (ironically, one that I later worked for and could get free classes at. PhD programs however, are full-time).
Today I have three publications that I'm 2nd author on, and in 2015 I attempted to get into school once again. I contacted several professors. Most simply don't write you back, but even when I got in touch with several schools, many simply didn't have any professors who were willing to take students in my field (environmental sensor research).
It's really competitive to get back into school and there is a massive disconnect right now between industry and academia.
You get out of any education what you put into it. You can leave with just a basic understanding of computer science and only know two languages leaving an outstanding program. You can also go to a crap program and push yourself to learn more on your own; using what professors teach as a jumping board for a lot more.
The TL;DR I'm getting at is that masters programs do have a purpose: getting you into a PhD program. If your work pays for it, it might be worthwhile for the additional title, but if not, you're not going to learn anything you couldn't apply yourself to on your own.
> The classes were of very poor quality - it was clear that they were a low priority for most faculty.
I took a master's in aerospace engineering at a top tier school and I wonder if this is the norm for grad engineering courses, and for many lower-level undergrad courses as well (think massive freshman calculus lectures). To calibrate what you consider "poor quality," how did you find the quality of your undergrad engineering courses?
This is great news. However, anyone who's used Piazza (the main "classroom" tool for OMSCS) knows that it's hardly ideal. I think a better collaboration/discussion tool is imperative to making the experience better for the "average" student.
Sure the top students in the program are going to do well, by definition, but there are plenty of more "middling" people like myself that can only be brought up to the next level with proper discussion/interaction with classmates. From my experience even PHPbb would be a more effective tool than Piazza.
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Suggestions (if Piazza folks are reading):
1. Allow one to delete follow-ups.
2. Allow students to create private "study group"-like
threads that aren't in the main feed.
3. Make it easier to upload pictures and other content.
4. Make things live. Normally this wouldn't be necessary, but anyone in the program knows many students post the same thing at the same time as a response to an event (like an email). By doing this you prevent redundant threads from being created.
5. Use some sort of up/down voting system that way the community can self-regulate.
There are plenty more things I'd improve, but for the sake of brevity those are some I just came up with on the spot.
edX (the partner through which Georgia Tech will be offering this program) uses its own homebuilt forums/discussions software, which offers many of the features you list. It's also open source, so if you or others have more discussion features in mind and have Python, Ruby or Backbone experience, PRs are welcome!
Disclaimer: software engineer at edX, have worked on discussions features in the past. Happy to answer questions.
I have taken over 30 courses on edX in the past few years (many more on Coursera). Some of those courses used Piazza, most the edX forums, so I know both systems. I also was a Community TA so I know the (few) options a CTA gets on edX.
Overall, IMHO Piazza clearly has far more features.
That said, I still prefer edX forums simply because I hardly ever need any of the Piazza features - and the Piazza GUI IMHO is much worse than edX especially for the target group. After edX implemented the sort-option "by activity" and "show only unread" my major complaints were solved.
--
By the way, since I hope you read this reply, the worst by far on edX is that the darn page just won't stand still!
Please stop the "convenience scrolling (by Javascript)". Also, the height of the page changes when I select a comment that does/doesn't fit in the viewport (long threads vs. no-responses-yet comments, for example, and that too triggers automatic scrolling.
Next, the size of the text box changes! I use Chrome to make it much larger to see my entire long and carefully crafted and formatted comment, then I switch to another tab to do more research for my reply, but when I click back into the textbox it shrinks back to its small default size. Could you please leave my manually-made-large textbox as it is?
No changes of scroll positions, no changes of sizes, no changes of anything, please. Not just in the forum, the course pages have the same annoying (anti-)"feature". I find it very inconvenient when the GUI changes under me, it's the GUI equivalent of walking on ice.
I would also enjoy a feature to "mark all as read", so that I can use "show only unread" at any point in time. Right now that feature forces me to have clicked on each and every comment before it is useful.
Life update would be "nice to have" too, especially for a TA (if it's too expensive for server-load, how about offering it just for the TA and STAFF user groups).
What is also missing is even the slightest hint (in the comment editor, or anywhere) that you support LaTeX style formulas! As well as some help for beginners how to use it.
I know Christmas was last month, but that's my wishlist :-)
What I don't miss are votes. That always gets misused, and it is of very limited benefit. I see the value of votes in forums on something like reddit, I don't see benefit in edX forums with a focus on Q&A. Especially not down-votes. Even the "pin to top" doesn't really work: At least 70% of users don't ever notice pinned posts. If there have to be votes, do it like Disqus: You can see who voted. Or instead of votes do what Github did in the issues comments.
By the way, why do you still have votes at all? They are only displayed if anyone specifically checks them in a given comment. They used to be shown in the comment overview on the left. Right now the "vote" feature can be removed and nobody would notice.
Some random ideas:
Feature: Allow Staff and TAs to post without their status. Just like on reddit. Not everything I had to say was "official", and one reason I turned down several invitations to be a CTA on edX was because I really hated to have a green banner around every single one of my posts. Too much pressure, and just because I'm TA doesn't make all my answers "precious".
Feature: Allow admins (TAs, Staff) to merge threads. For example, the introductions at the beginning where 500 people post their own "glad to be here" thread instead of using the pinned(!) "Post your introduction here" thread, or when there is an issue and 50 people each report it (even when the last 20 new threads are about that same subject, most people post immediately before looking at the forum).
Feature: Detect if you responded to this person in the past. It's nice and gives a feeling of familiarity to find out you talked to someone in the past.
Feature: Geolocation. Show where the other people (in the current thread) are located.
Feature: Combine Wiki and forum: From within the forum let people select comments and/or threads for the Wiki. Ask the for some additional information, like choosing a Wiki page/section. This can address the problem that good comments are quickly buried by all the new ones.
Bug report: It seems right now the "Section" or "Chapter" or whatever you call it is not displayed in comments.
For almost every course I've taken in OMSCS, I always run into those issues. I'm pretty sure Piazza team knows about these problems too, because I had these issues since I was taking courses in undergrad (4-5 years ago). What this makes me think is that Piazza is just incapable of improving its own core product because it's struggling to monetize and generate revenue. I think Piazza in its current state just doesn't work at the scale of OMSCS. It could work with class size of maybe 30-40 students. I would gladly switch over to another product if it was offered as an option.
As a current OMSCS student I have to agree that Piazza is less than optimal. I've found that in order to be successful I have to put in significant effort to communicate across all channels - Piazza, Slack, Hangouts, HipChat, email, Skype.
The classes do vary in difficulty and polish. I definitely have had some negative experiences with students that weren't prepared or weren't doing the necessary reading and studying.
We're using Slack the product design course I'm teaching at University of Michigan this semester[1]. Not only does it do that stuff better than Piazza, but it'll get students used to the tools startups use for communication (though I have my issues with Slack).
Your second point rubs me the wrong way, a little bit. You probably didn't mean it like this, but... I think educators should have the education in mind first, and employability second. In this case, nobody is going to put Slack on their resume, so... I just find the argument strange
When I first started doing Udacity they used Piazza which was okay. I believe OMSCS is going through Udacity also. We eventually switched over to Discourse I believe and it addresses most of your concerns. The search and duplicate checking before posting is great.
Many of the students and TAs are also on the slack group, omscs-study. It's far from ideal because it's "non-official" but it addresses a lot of these complaints.
Piazza has been a pretty good tool IMO. I haven't used it in the context of the OMSCS, only in all my undergrad classes at Berkeley, but it's been a really helpful tool when there's good moderation from the Professors and TAs.
Maybe this condition does not hold true for the OMSCS, but I think almost all your problems can be solved by a good moderation team.
I agree with you about Piazza. I was always very active during the OMSCS program on Piazza, but it didn't seem ideal for those "middling" folks. The biggest problem was important discussions and ideas getting buried. There just wasn't a good way to keep track of it all.
I'm currently in the Master of Science in Analytics program at gatech, so if anyone has any questions feel free to ask.
My experience so far has been excellent. I just started my second semester, and I can say that the curriculum covers exactly what I wanted to learn with the exception of one class. The program is extremely practical, it's only one year and is focused on getting the students jobs. The professors are great, and I highly recommend it to anyone wanting to get into the field.
How much statistics is involved? I'm asking because I have a friend who is interested in math and statistics but wants to go to grad school for something a bit more practical, while still having some challenge in terms of statistics.
I'd say it's very statistics heavy, but it depends on the individual. They offer three different tracks: computational, business, and analytical tools. If you pick the analytical tools track your classes will be more stats heavy.
Most of the core classes (like machine learning) are more math based. In machine learning, it's done from a mathematical/theoretical approach. Another example is regression analysis, where you learn the math before doing the practical work in R.
I have a weak math/stats background but a strong software background, which also aligns with my interests so I'm doing the computational track. As I mentioned before, most of the core curriculum is math heavy so I'm learning that aspect, but I'm also getting a ton of practical experience in machine learning and big data work.
- CS7450: Information Visualization. Basically learned how to create useful data visualizations. Loved this class
- CSE 6242: Data and Visual Analytics. This class is interesting because you learn a very wide breadth of tools. We learned visualization (D3), big data processing (hadoop), and analytic tools (random forests for example). This was my favorite class so far
- ISYE 6414: Regression Analysis. The theoretical math parts of this class were really hard for me, but learning how to do practical regression analysis in R was super valuable to me.
- ISYE 6644: Simulation. Loved the professor for this class (Dr. Goldsman) but the material didn't seem very useful to me. Knowing the general approach of simulation for modeling is useful, but we went pretty deep into the math that I don't think will be useful in the long run
- ISYE 8803: Intro to Analytics. This was the most practical class I took and was taught by the head of the program. You basically learn all of the analytical models for the first 2/3s of the class, then in the last third you look at case studies and how to apply them.
Hopefully there's enough detail in there for you, let me know if you have any other questions
Not sure I can speak to this question since I'm not affiliated with admissions at all, but I can tell you the makeup of my cohort.
Most (I'm guessing 75%, possibly higher) have some work experience. Most have either strong math background or strong software background but there are exceptions. Lots of former engineers of all kinds, chemical, mechanical, software, etc.
Can anyone speak to the value of the general online CS Masters degree offered by Georgia Tech? I'd been considering it, as it allows you to keep working while you improve your resume. I'm curious if employers (1) can tell it was completed online and (2) distinguish between individuals getting the residential MA versus the online MA. Thanks in advance.
"Will the degree I receive from the OMS CS program be the same as the on-campus MS in Computer Science or will my degree say “Online”?
Your diploma will read "Master of Science in Computer Science," exactly the same as those of on-campus graduates. There will be no "online" designation for the degrees of OMS CS graduates."
An employer could simply notice that your location and your school are in different places. However, IMO it's more impressive to have been working a full time job and doing a degree than the opposite.
I did the MSCIS program through Boston University several years ago, and this has been my experience. Employer's will figure it out, but they actually tend to be more impressed once they find out I was working full time and going to school.
I live in Atlanta and work with a bunch of Tech grads. Tech is one of the most prestigious engineering universities in the southeast (if not the most), and its degrees are tremendously valuable.
AFAIK, this is Georgia Tech's second online masters degree course, which demonstrates that the university now stands behind their online degree program. One of my coworkers is currently enrolled in their other online masters program, and he speaks highly of it.
If this is anything like their first online masters program, there is no distinction between the online and on-campus diploma. The degree is a full-fledged Georgia Tech degree. This is what makes it so attractive--employers and other educational institutes won't know you didn't physically attend the campus.
I'd be happy to get more of my coworker's opinion on the program if you like. I'm interested in it myself...
While technically GA Tech makes no distinction between the two degrees, in practice employers can often tell. However this isn't always a bad thing.
I'm a current student and I switched jobs about halfway through the program. I'm living in Austin so it came up in the interviews that this was an online program. I explained to people that I was doing the online degree in my spare time but taking it slower and that all the courses were the same as the on campus counterparts. Generally speaking the interviewers seemed to be impressed by this and said that it showed a strong work ethic.
I can't give hard facts about the value of the program but given that it only costs between $7k and $10k to complete I think it will pay for itself very quickly and perhaps already has.
There is a good podcast/audio interview[1] with C Charles Isbell, Jr., Professor and Senior Associate Dean at GA Tech., on the TWiML podcast. He discusses how their first online master's program has worked, and I found it quite informative.
> And everyone who's been exposed to both says it's the same courses and rigor as the on-campus version.
I am curious: who in the world would do both?
A more serious question, since you're going through it now: is there a thesis component to the online MS, or is it just coursework? If there's a thesis (either required or as an option for more research-oriented students), how does the thesis mentoring at a distance work?
I did a resident MS CSE around the time they started the online program. While for the classes that were also available online they seemed equivalent (usually just recorded versions of the same class), there is one big caveat: most of the courses I took during my MS, or at least most of the most valuable courses I took, were not offered online.
My BS CS was also from GT (several years before), so much of the core course list available online were slightly more rigorous retreads of distributed algorithms, simulation architectures, etc. already covered late in undergrad. The most valuable courses in my MS were special topics courses that, at least at the time, weren't available online, many of which were in other departments as part of the computation + application domain interdisciplinary approach of the program.
That said, the core courses that were also offered online were generally quite good, and a few were exceptionally great. Rich Vuduc's HPC applications course comes to mind.
My wife and I have done some of the same coursework (for different degrees), me on campus and hers online. I actually would have preferred online. Rigor seemed equivalent, but I find the ability to pause and rewind lectures invaluable.
A colleague is nearly complete with his online CS Masters from GT. He has spoken well of it, so much so that another colleague has already applied and been accepted. I considered it as well, but I'm tired of traditional school and don't see a need for the MS when a BS is doing me fine. Maybe some day.
So based on what I've seen and read, I could recommend it.
Having taken a number of MOOCs as well as having a traditional engineering masters (not CS), I'm inclined to agree.
- Although not universally true, you tend not to need labs stocked with equipment that aren't practical for an individual to purchase. (Though, yes, CS degrees can be oriented around hardware.)
- Automated grading of problem sets/tests seem to work better with software than just about any other topic of MOOCs I've seen. (Though that's probably not an issue for this sort of paid program as you can have paid TAs and professors directly involved.)
- Remote "teams" can clearly collaborate effectively on projects etc. as demonstrated by the fact that they do in many companies and on many open source projects. That said, a big part of a masters degree is going to be individual work anyway.
ADDED: As someone else mentioned, in-person mentoring would still be a concern of mine but that's probably a tractable issue.
I'm waiting for the online, low-cost undergraduate degree of comparable quality to the "real thing". Maybe it's already here. I recently checked up on tuition at my alma mater and almost barfed. With a kid on the way Mr. Market has 18 years to figure this out for me.
I am skeptical that this program will be a good signal for hiring analysts. In my experience, there are two things you need to select for:
(1) Understanding statistics. Hopefully this program will take care of this requirement, but it's not hard to find these people anyway. There is an infinite supply of science PhDs fleeing the academic job market.
(2) Behavioural/personality. People who will do well at the actual job. Example: can you tell when a PM is asking you to answer the wrong question, and how do you handle it?
You can easily find (1) with screening questions, (2) is the hard part.
But, I guess if you think you have (2) as a future analyst, this program could be a good way of getting (1).
Is there anything similar to the Online Masters but a Bachelor's degree? Most of the online universities like WGU seem like a get a bunch of certificates then you graduate.
I went to WGU and graduated with a Business Management degree. I did my homework before I went and the degree is fully accredited, with some students moving on to graduate programs. I only got one certificate during the entire experience and that was through CompTIA when one of their tests counted as the final exam.
Readit News
I know people have many reasons to get a Masters. If your goal is to take some higher-level classes, you can do better than GT. If you are self-motivated enough to do an online degree, you can do it for free. Aside from free offerings from sites like Coursera, you can find whole courses up from many institutions - with syllabi, lecture slides, homework assignments, etc.
If you're planning to do it for the salary, in my experience the degree ended up being worth a $95K to $115K bump in starting salary. Compare this with the 2 years of industry salary that I would have received, and the 2 years of experience (and raises that come with that). I know I wasn't paid better than the folks who had been with the company for 2 years more than me.
If you're thinking about it for the sake of your resume, I do hiring screening / interviews now (for Data Science and Software Engineering positions) - and I really don't care if the applicant has an MS or not (or what classes they may have taken). Most folks I know that do hiring think similarly. My main signal from your resume is projects you've been on and how you contributed.
From my point of view, this program is a losing proposition for any potential student.
Yes, you can study the same material on your own, but you won't earn a degree from it. Now that I've got the degree, I'm in much better shape to pursue further learning on my own.
Note, however, that I didn't do this to improve my resume, go fishing for a new job, or try to get a raise. With tuition reimbursement from my company I only spent $3500 over 2 1/2 years to earn a full-fledged master's degree.
Based on the above, I can't agree that it's a losing proposition.
Could you explain a bit more what do you mean with this statement? Is it that you feel better prepared to study advanced topics (like advanced ML/Data Science) or was the degree a requirement for something else you wanted to pursue?
I'm curious about what other "doors" having this degree opens, other than the bump in salary mentioned by others.
Congrats for completing the program btw.
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. 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. I was planning on pursuing a PhD when I started into my MS, so this format worked quite well for me at the time. 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[0].
It's interesting that you brought up machine learning. Charles Isbell's Intro ML class was a significant exception to the pattern I described above. In addition to high quality, pre-prepared lectures peppered with entertaining anecdotes, the had high quality projects that worked with pratcial tooling. It was also probably the highlight of my graduate career[1].
[0]: In particular, the material covered in my graduate systems classes has been invaluable for not reinventing the wheel for the thousandth time. The material from the couple compilers classes I took on a whim has been a huge boon when talking about software correctness. I work on the hypervisor underneath GCE. Correctness is near and dear to my heart, but performance is right there with it :)
[1]: 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. In particular, my mindset shifted to one of models of computation and decomposition of problems into subproblems for which the simplest model could apply. I have an example I'd like to write up, but it's a bit long for a footnote.
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.
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.
The question remains is if an online degree has the same credibility. Looking back at my time at GT, I cannot see how operating solo, without the constant feedback from your peers and faculty, is as good. There is more than just what is in the study material. The other question is if the entrance requirements are still as stringent.
From my perspective, unfortunately, Georgia Tech has really diluted the value of their masters in cs degree. They have become a sort of immigration visa-mill with very many India undergrad -> Georgia Tech Masters of very questionable skill level.
Just my experience.
From my experiences with GT, I am apprehensive about the quality of content - I assume it's coming from the same departments and professors that I had experience with.
From other comments here it seems that the approach this program took was quite different from the one employed on campus, so that apprehension may be unfounded - that is, the online offerings may be of higher quality than what students on campus receive. Still, I felt like I needed to post something to warn people that the branding of GT does not in-and-of-itself mean that the content will be of high quality. Students considering the program should try and find some way to evaluate this - are there sample classes or lectures posted online? Perhaps ask someone you trust in the industry to take a look and give you their feedback.
Still, I think students who are self-motivated should consider what they would be able to accomplish if they took some time to organize a study program for themselves. There are many free high-quality resources out there that could be used for effective self-directed study.
Students who are less confident about doing it on their own should be asking themselves what sort of support they expect to be getting from the program. Certainly there are many advantages in having things curated for you, as well as having access to discussion boards with other students going through the same material. Aside from that, many students (unfortunately, I think), need the external schedule and commitment - and for them, merely having an exam deadline, or the $10K investment looming in the background may be the thing needed to get through the material. Those students, too, should be realistic about the investment they are making and what they hope to get out of it.
>Andrew Ng's Coursera class on Machine Learning was the pedagogical highlight of my time at GT, and I did it on my own initiative (and it's free).
This is something I wanted to highlight from your post. I don't think this is surprising, nor do I think it is reasonable to state that a course (or a degree program) is poor quality because it didn't meet the standards of Ng's ML course. That is an exceedingly high bar.
Its something I noticed, because I am a recent grad, so while I was in my mid-level courses, and had recently taken Tech's intro CS course, I was able to watch (Harvard's) CS50 and other courses. But on the other hand, I've seen some very bad online courses. The successful and large online courses are successful and large specifically because they are head and shoulders better than the rest. And there are a lot of decent online courses, so to measure against what are some of the absolute best online courses is to measure against courses that have more resources, more planning, and more feedback than most.
(as an aside, they also have more incentive to be good, but that's a bit tangential to the point that they also have more opportunity to be good).
You type in "Machine Learning" on coursera and you get over 1000 results (not all of which are relevant, but assuming even 10% are), its little wonder that one or two are going to be better than the even the best courses that you'll take during a bachelors or masters, because Coursera offers more Machine Learning courses than most people will take in their Bachelors or Masters.
Combine that with these courses coming prefiltered (you've heard of the Stanford Course, but what about "Applied Text Mining in Python" from UMichigan, which for all I know might be great, but it doesn't come with the hundreds of recommendations that the Ng course does, so I don't know that it will be great) and you have a really great recipe for a bias against the in person courses.
What track were you on? Do you think that had anything to do with it? What did your peers think about the program?
A $95 to $115k bump sounds pretty darn good. Did you already have a CS undergrad degree? Where from? Sorry for all the questions, feel free to share as little or as much as you're willing.
I'd say my peers generally shared my opinion (classes not being very good). Many of them were trying to get into PhD programs so they were focusing on finding research opportunities and didn't care about the quality of classes much. Some struggled but blamed themselves for this rather than the class. (I'd say this was very common amongst the undergrads too).
Of the PhD students I knew, most were discouraged from taking classes altogether, since it took away time from research. This was true even in the first year of their program. The general attitude from that side was that classes were a waste of time.
Many higher-level classes were run as mini-research projects. You'd get some content, then the rest of the class would be forming teams, proposing project ideas, implementing, writing up results and having 'mini conferences'. I think faculty liked this since it was a good way to try out research ideas, recruit potential PhD students, and give their current students extra time to work on their research.
Nothing wrong with that format, of course, but the actual coverage of content was typically superficial. If you weren't already familiar with the area, you had to figure it out on your own as you went along. Also, this is not a class format that translates to online very well.
Also, hang in there. The degree may not have helped your current job as much as you'd like, but it may help more at a future company. Sadly we often need to change jobs to get a real salary bump.
The margins on adding a MS CS (or analytics) is less for a CS grad, but at 10k a degree and the flexibility of taking online they are huge for non-CS majors looking to pivot. 10k to bump your salary up 20k (while working and getting experience + raises) plus the knowledge add pays for itself.
Im biased on account of being in the program (supplementing my Electrical Engineering degree due to a change in career and life paths), and I know a lot more about graph theory, formal algorithms, and high efficiency computing than I did a year ago. These are things that help improve both your portfolio of rigorous projects (Im currently working to port over all of my high performance computing assignments to Rust) and assortment of tools for the ever annoying CS interview.
My doctorate was at Texas, and it certainly catered primarily to what brings in the $$: e.g. research.
I didn't have a Computer Science undergrad degree, so perhaps I have a different outlook on this than you did. I'm currently working as a software engineer, so I'm also not forgoing earning a living by taking the time off from school.
In other words, while I can understand why you feel disappointed by your educational experience, there are other lenses through which this program makes sense. I feel good about having gone through it so far and I'm looking forward to finishing.
However.... I think majority of people that take OMSCS (I am in my 2nd semester) and this new OMSA program do so part time while still maintaining their fulltime jobs and families.
Also, why the throwaway?
I'm getting exactly what I'm looking for out of it (a somewhat structured environment to learn in), so for me I would say this isn't a losing proposition.
Your situation is anecdotal at best. You are critiquing an in person experience to an online experience.
GT goes out of its way to say that the online experience and physical experience offer the same degree, is it not fair to compare them?
Anecdotally I've felt all of the professors are eminently interested in interacting with MS students online.
Although I agree with your sentiment (formal education has a lot of flaws), a full-fledged degree potentially solves other problems than just improving your resume to get a better/higher paying job for engineers.
Family pressure, lack of confidence that comes with not having a formal CS undergraduate degree, or motivation and structure that comes from paying for a formal program could all contribute to someone choosing this path. A $10k, online masters program from GT seems like a good option for some people.
There are no programs that have stood out to me during hiring to predict the quality of the candidate.
If you're a student looking at programs, I would prioritize the amount and quality of individual attention you stand to receive. A self-directed student can do well anywhere (including on their own). If you're not so stubborn/resilient, having a good community and mentorship to help you overcome difficult times is key.
I could have done it for 1/3-1/4 of the cost and in the same amount of time but with far less time spent commuting.
Wait... isn't this kind of a good reason to suffer through the course?
From my first job, I realized life in a cube wasn't for me. I really wanted to be in front of a classroom. I realize there are problems with academia. I know you have the same squabbles and competition you have in the corporate world, and seeking certain grants to keep yourself afloat can cut into the research you actually want to do.
Still, I really wanted to teach. I've seen so many professors who only work one or two jobs, or go straight from BS -> MS -> PhD with very little industry experience. I wanted to be a different type of professor with plenty of real work experience to drawn on and teach from.
Grades don't matter. I've found that's very true for industry. Having a GPA on your CV doesn't really mean anything and most people leave it off. However, it has a huge impact on getting into degree programs.
I only had a 2.5 undergrad and even though I got a 3.2 in grad school, it wasn't enough for most programs I looked at. I attempted and failed to get into 8 schools back in 2009 (ironically, one that I later worked for and could get free classes at. PhD programs however, are full-time).
Today I have three publications that I'm 2nd author on, and in 2015 I attempted to get into school once again. I contacted several professors. Most simply don't write you back, but even when I got in touch with several schools, many simply didn't have any professors who were willing to take students in my field (environmental sensor research).
It's really competitive to get back into school and there is a massive disconnect right now between industry and academia.
You get out of any education what you put into it. You can leave with just a basic understanding of computer science and only know two languages leaving an outstanding program. You can also go to a crap program and push yourself to learn more on your own; using what professors teach as a jumping board for a lot more.
The TL;DR I'm getting at is that masters programs do have a purpose: getting you into a PhD program. If your work pays for it, it might be worthwhile for the additional title, but if not, you're not going to learn anything you couldn't apply yourself to on your own.
Dead Comment
I took a master's in aerospace engineering at a top tier school and I wonder if this is the norm for grad engineering courses, and for many lower-level undergrad courses as well (think massive freshman calculus lectures). To calibrate what you consider "poor quality," how did you find the quality of your undergrad engineering courses?
Sure the top students in the program are going to do well, by definition, but there are plenty of more "middling" people like myself that can only be brought up to the next level with proper discussion/interaction with classmates. From my experience even PHPbb would be a more effective tool than Piazza.
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Suggestions (if Piazza folks are reading):
1. Allow one to delete follow-ups.
2. Allow students to create private "study group"-like threads that aren't in the main feed.
3. Make it easier to upload pictures and other content.
4. Make things live. Normally this wouldn't be necessary, but anyone in the program knows many students post the same thing at the same time as a response to an event (like an email). By doing this you prevent redundant threads from being created.
5. Use some sort of up/down voting system that way the community can self-regulate.
There are plenty more things I'd improve, but for the sake of brevity those are some I just came up with on the spot.
Disclaimer: software engineer at edX, have worked on discussions features in the past. Happy to answer questions.
Overall, IMHO Piazza clearly has far more features.
That said, I still prefer edX forums simply because I hardly ever need any of the Piazza features - and the Piazza GUI IMHO is much worse than edX especially for the target group. After edX implemented the sort-option "by activity" and "show only unread" my major complaints were solved.
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By the way, since I hope you read this reply, the worst by far on edX is that the darn page just won't stand still!
Please stop the "convenience scrolling (by Javascript)". Also, the height of the page changes when I select a comment that does/doesn't fit in the viewport (long threads vs. no-responses-yet comments, for example, and that too triggers automatic scrolling.
Next, the size of the text box changes! I use Chrome to make it much larger to see my entire long and carefully crafted and formatted comment, then I switch to another tab to do more research for my reply, but when I click back into the textbox it shrinks back to its small default size. Could you please leave my manually-made-large textbox as it is?
No changes of scroll positions, no changes of sizes, no changes of anything, please. Not just in the forum, the course pages have the same annoying (anti-)"feature". I find it very inconvenient when the GUI changes under me, it's the GUI equivalent of walking on ice.
I would also enjoy a feature to "mark all as read", so that I can use "show only unread" at any point in time. Right now that feature forces me to have clicked on each and every comment before it is useful.
Life update would be "nice to have" too, especially for a TA (if it's too expensive for server-load, how about offering it just for the TA and STAFF user groups).
What is also missing is even the slightest hint (in the comment editor, or anywhere) that you support LaTeX style formulas! As well as some help for beginners how to use it.
I know Christmas was last month, but that's my wishlist :-)
What I don't miss are votes. That always gets misused, and it is of very limited benefit. I see the value of votes in forums on something like reddit, I don't see benefit in edX forums with a focus on Q&A. Especially not down-votes. Even the "pin to top" doesn't really work: At least 70% of users don't ever notice pinned posts. If there have to be votes, do it like Disqus: You can see who voted. Or instead of votes do what Github did in the issues comments.
By the way, why do you still have votes at all? They are only displayed if anyone specifically checks them in a given comment. They used to be shown in the comment overview on the left. Right now the "vote" feature can be removed and nobody would notice.
Some random ideas:
Feature: Allow Staff and TAs to post without their status. Just like on reddit. Not everything I had to say was "official", and one reason I turned down several invitations to be a CTA on edX was because I really hated to have a green banner around every single one of my posts. Too much pressure, and just because I'm TA doesn't make all my answers "precious".
Feature: Allow admins (TAs, Staff) to merge threads. For example, the introductions at the beginning where 500 people post their own "glad to be here" thread instead of using the pinned(!) "Post your introduction here" thread, or when there is an issue and 50 people each report it (even when the last 20 new threads are about that same subject, most people post immediately before looking at the forum).
Feature: Detect if you responded to this person in the past. It's nice and gives a feeling of familiarity to find out you talked to someone in the past.
Feature: Geolocation. Show where the other people (in the current thread) are located.
Feature: Combine Wiki and forum: From within the forum let people select comments and/or threads for the Wiki. Ask the for some additional information, like choosing a Wiki page/section. This can address the problem that good comments are quickly buried by all the new ones.
Bug report: It seems right now the "Section" or "Chapter" or whatever you call it is not displayed in comments.
[1] http://umichdesign.com/
Maybe this condition does not hold true for the OMSCS, but I think almost all your problems can be solved by a good moderation team.
My experience so far has been excellent. I just started my second semester, and I can say that the curriculum covers exactly what I wanted to learn with the exception of one class. The program is extremely practical, it's only one year and is focused on getting the students jobs. The professors are great, and I highly recommend it to anyone wanting to get into the field.
Most of the core classes (like machine learning) are more math based. In machine learning, it's done from a mathematical/theoretical approach. Another example is regression analysis, where you learn the math before doing the practical work in R.
I have a weak math/stats background but a strong software background, which also aligns with my interests so I'm doing the computational track. As I mentioned before, most of the core curriculum is math heavy so I'm learning that aspect, but I'm also getting a ton of practical experience in machine learning and big data work.
- CS7450: Information Visualization. Basically learned how to create useful data visualizations. Loved this class
- CSE 6242: Data and Visual Analytics. This class is interesting because you learn a very wide breadth of tools. We learned visualization (D3), big data processing (hadoop), and analytic tools (random forests for example). This was my favorite class so far
- ISYE 6414: Regression Analysis. The theoretical math parts of this class were really hard for me, but learning how to do practical regression analysis in R was super valuable to me.
- ISYE 6644: Simulation. Loved the professor for this class (Dr. Goldsman) but the material didn't seem very useful to me. Knowing the general approach of simulation for modeling is useful, but we went pretty deep into the math that I don't think will be useful in the long run
- ISYE 8803: Intro to Analytics. This was the most practical class I took and was taught by the head of the program. You basically learn all of the analytical models for the first 2/3s of the class, then in the last third you look at case studies and how to apply them.
Hopefully there's enough detail in there for you, let me know if you have any other questions
Most (I'm guessing 75%, possibly higher) have some work experience. Most have either strong math background or strong software background but there are exceptions. Lots of former engineers of all kinds, chemical, mechanical, software, etc.
Dead Comment
"Will the degree I receive from the OMS CS program be the same as the on-campus MS in Computer Science or will my degree say “Online”? Your diploma will read "Master of Science in Computer Science," exactly the same as those of on-campus graduates. There will be no "online" designation for the degrees of OMS CS graduates."
https://www.omscs.gatech.edu/prospective-students/faq
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An employer could simply notice that your location and your school are in different places. However, IMO it's more impressive to have been working a full time job and doing a degree than the opposite.
AFAIK, this is Georgia Tech's second online masters degree course, which demonstrates that the university now stands behind their online degree program. One of my coworkers is currently enrolled in their other online masters program, and he speaks highly of it.
If this is anything like their first online masters program, there is no distinction between the online and on-campus diploma. The degree is a full-fledged Georgia Tech degree. This is what makes it so attractive--employers and other educational institutes won't know you didn't physically attend the campus.
I'd be happy to get more of my coworker's opinion on the program if you like. I'm interested in it myself...
I'm a current student and I switched jobs about halfway through the program. I'm living in Austin so it came up in the interviews that this was an online program. I explained to people that I was doing the online degree in my spare time but taking it slower and that all the courses were the same as the on campus counterparts. Generally speaking the interviewers seemed to be impressed by this and said that it showed a strong work ethic.
I can't give hard facts about the value of the program but given that it only costs between $7k and $10k to complete I think it will pay for itself very quickly and perhaps already has.
1. https://twimlai.com/twiml-talk-4-charles-isbell-interactive-...
And everyone who's been exposed to both says it's the same courses and rigor as the on-campus version.
(Source: 2 classes from finishing)
I am curious: who in the world would do both?
A more serious question, since you're going through it now: is there a thesis component to the online MS, or is it just coursework? If there's a thesis (either required or as an option for more research-oriented students), how does the thesis mentoring at a distance work?
My BS CS was also from GT (several years before), so much of the core course list available online were slightly more rigorous retreads of distributed algorithms, simulation architectures, etc. already covered late in undergrad. The most valuable courses in my MS were special topics courses that, at least at the time, weren't available online, many of which were in other departments as part of the computation + application domain interdisciplinary approach of the program.
That said, the core courses that were also offered online were generally quite good, and a few were exceptionally great. Rich Vuduc's HPC applications course comes to mind.
So based on what I've seen and read, I could recommend it.
- Although not universally true, you tend not to need labs stocked with equipment that aren't practical for an individual to purchase. (Though, yes, CS degrees can be oriented around hardware.)
- Automated grading of problem sets/tests seem to work better with software than just about any other topic of MOOCs I've seen. (Though that's probably not an issue for this sort of paid program as you can have paid TAs and professors directly involved.)
- Remote "teams" can clearly collaborate effectively on projects etc. as demonstrated by the fact that they do in many companies and on many open source projects. That said, a big part of a masters degree is going to be individual work anyway.
ADDED: As someone else mentioned, in-person mentoring would still be a concern of mine but that's probably a tractable issue.
http://www.londoninternational.ac.uk/
Three of my kids and I are are doing CS degrees from here right now, two of us while working full-time.
https://www.coursera.org/university-programs/masters-in-comp...
(1) Understanding statistics. Hopefully this program will take care of this requirement, but it's not hard to find these people anyway. There is an infinite supply of science PhDs fleeing the academic job market.
(2) Behavioural/personality. People who will do well at the actual job. Example: can you tell when a PM is asking you to answer the wrong question, and how do you handle it?
You can easily find (1) with screening questions, (2) is the hard part.
But, I guess if you think you have (2) as a future analyst, this program could be a good way of getting (1).
http://www.wgu.edu/online_it_degrees/data_management_analyti...