GA Tech vs. OU (which program?)

Wow. I haven’t blogged in a long time. To be fair, it’s been busy and the time I have had, I’ve chosen to spend on time relaxing. I’m only taking a few minute just now because the grad-school prep has kicked into high gear, and I have some thoughts on the process.

I wasn’t sure what I might expect in applying for grad programs. I did know that I needed an asynchronous online program delivered by a good and preferably recognizable school. A traditional education environment wouldn’t really work for me. After researching programs for a couple weeks, I narrowed the list down to 3 top pics: Georgia Tech (GA Tech), University of Oklahoma (OU), and Michigan Tech. I was also in touch with recruiters from three other schools that didn’t really fit my need. One of them appears to be more of a degree mill than the kind of program I want. There were two more I hadn’t quite gotten around to calling, but they were more on the expensive end and would be down the list. In any case, I put in for GA Tech first (my top choice), then OU (University of Oklahoma). I never got around to my #3 pick because OU accepted me before the ink was dry on my last letter of reference.

Many weeks after my initial application, GA Tech came through with an acceptance letter. Meaning I got into both schools and I had to make a choice. For me, it a huge surprise. Yes, I had really good reference letters, brought a pretty robust track record in data management and statistics, but I’ve been out of school for about 20 years. For OU, I was really confident about my application package. Given their acceptance rate, I reckoned I had a really solid shot. GA Tech, however was a different story. To start, it’s a notoriously difficult school to get into (23% acceptance rate?), and the Online Masters program is no different than on-campus. While I do have a good work-record, I was worried that my ‘pretty okay’ GPA and (while I think it was fantastic), relatively unremarkable university degree were going to work against me. In any case, I made the cut, and I’m feeling really good about that.

Anyhow, I got into both schools I applied to, so why did I pick GA Tech? That’s actually what I wanted to write this blog about. Perhaps you’re also looking at a data science degree and what to know why I picked one over the other? To be sure, I haven’t started courses yet so the relative value isn’t known, it’s just sort of anticipated, but I do expect to be challenged and learn a TON (that’s the point, right?).

The whole reason I want to get the MS is to expand my knowledge and skills in the realm of data science. Not just statistics or biometrics, but sort of overall. I’m a senior research analyst, but I actually don’t have the full background I’d need to go to another organization if I needed to (you never know, I’ve got no plans to leave, but I do have a powerful need to keep a paycheck). Plus, those skills can help us my current organization tackle research questions with even more capacity and knowledge. Just the other day, I was asked about a meta-analysis of income. This is a master’s thesis on it’s own, and it’s on my radar to just sort of slip it in at some point… How much better would it be if I took that database of well over a million rows and hundreds of variables and hit it with a giant statistical baseball bat? That’s the stuff grant proposals are made of. Anyhow, that was my frame of mind when looking at these.

OU (University of Oklahoma)

So, what did I think of OU? It looks like a ‘solid’ program. I can say that their student success game is good enough that I seriously considered just staying on track there. The program has an ‘average’, if slightly more competitive price-tag – About $32K USD in total (it was a bit more and I think subject to change, but it’s a university, that’s how it goes). The key for me, however, was the mix of coursework The classes I’d have taken are:

  • Required Classes:
    • Computing Structures
    • Algorithm Analysis
    • Advanced Analytics and Metaheuristics
    • Fundamentals of Engineering Statistical Analysis
    • Database Management Systems
    • Intelligent Data Analytics
    • Professional Practice
  • Electives (probably, it’s not clear what is offered in any given semester based on the materials I have)
    • Financial Engineering Analytics
    • Bayesian Statistics
    • Time series Analysis (I think? – this may not have been offered in a way I could do it)
    • Introduction to R
    • Advanced R
    • Data visualization

The total list of courses offered were fine for this kind of program, but missed some key elements, while involving things I’d like to swap out. Tor example the financial stuff and database management systems are low-priorities for me. Further, I don’t know that R would’ve been the best use of my time because that’s something I can learn on my own. Nevertheless, these remained the most valuable of the remaining courses.

Overall, the application and on-boarding experience was great, I was really impressed and never for a minute felt like I didn’t know what was coming. Thinking about that and the mix of classes, I’d describe This as the kind of program designed for people looking to transition into data and analytics from a related job or an entry level job to something more advanced.

This is a pretty luke-warm review of OU, but there is enough in there that It would have been a valuable learning experience. If you’re new to data science with some background or looking to jump up a level from junior or mid-level to a senior analyst, I think this is likely to be a really solid option and reasonably priced. And again, they have a VERY strong student success process.

Georgia Institute of Technology (Georgia Tech. / GT)

What was it about GA Tech? Well, if you’re researching these, you’ll probably have spotted that the price-tag clocks in at about 1/3 the usual price. I found it at $10K USD. It’s low enough, I even asked admissions to verify the price. That was the key attention getter and driver initially. Their admissions deadlines are a bit more stringent than other programs, and I found myself up against a deadline, so I applied before I’d fully digested other options.

I would describe the admissions process as ‘fine’, but a little slower on the information dissemination than I’d like, though it’s not proved a problem. I’ve received helpful responses to inquires in a timely fashion. One difference from OU was that this program also made it clear you need to be prepared with a strong STEM background to begin with, including pre-requisites such as Calculus, python programming, statistics and probability and Liner Algebra from previous schoolwork. This is also pretty typical among these programs, but OU placed less focus on that (Michigan Tech. my 3rd choice also emphasized these).

The course material, while still including things that aren’t essential for me, is very much in-line with my goals for this degree. Also, there are 3 tracks to help you focus your attention, I picked the Analytical Tools Track. The courses I’m going to try to take (again, not sure how scheduling works):

  • Required:
    • Introduction to Computing for Data Analytics (I may see about swapping this one out. The course description sort of suggests this is what I already do every day.)
    • Introduction to Analytics Modeling
    • Business Fundamentals for Analytics
    • Data Visualization Analytics
    • Data Analytics in Business
    • Applied Practicum
  • Electives
    • Regression Analysis
    • Bayesian Statistics
    • Probabilistic Models
    • Time Series Analysis
    • Computational Statistics

I think really, if you’re looking at something like this, unless you’ve already got an undergrad that’s strong in the maths or computer science, even getting accepted may be a challenge. It works for me, and I’ve got the background to launch in with an expectation of success. Term starts in about eight weeks, and I already have a significant amount of review and prep work to do (I’m about 3 chapters into my old Linear Algebra book at this point, and I’ve got lots more to do). I’m generally up to that challenge, as it is review, but I can see where some prospective students would be intimidated.

Other considerations: Neither program required GRE tests, OU is on a Fall-Winter-Summer schedule that appears to be partially aligned with the on-campus schedule, GT is fully aligned with a Fall-Winter schedule. Both programs are fully accredited and will result in a Master’s of Science in Data Science and Analytics. Both require a practicum, it appears OUs practicum comes from corporate partners or your current employer, where the GA tech one is less clear, seems to be either from your own job or they have ready-made projects that can be used.

So, that’s where I am. As I get into it and start really working toward this, I’ll be posting more about it, but for now, I’m going to unwind with some video games before bedtime.


Well, that was fast.

Okay, I’m not ready to say a lot. I’ve been thinking about it and wanted to write a pretty long and in-depth post, but the best I have is: I’m going to grad school in the fall. THIS IS GOING TO HAPPEN!!!! I’m not to a point where I can talk about the program or school, but as soon as the dust settles and enrollment happens, I absolutely will.

What I can say right now is that I’m stuck in between the notion of: “Hell yeah, I can do this, a good school with a good program wants me ASAP!!!” and – OMG, did I do enough research and are these programs what I *think* they are? Anyhow, one month out from getting serious, and it’s going to happen!

But, why though? – some thoughts about grad school.

This morning, I got a fantastic set of questions in response to my previous post about a mid-life career path. It boiled down to the following:

  1. Setting aside the practicality of work and a degree, do you have the literal time to make back the tuition cost?
  2. Could you consider a more-rapidly paced certificate program, perhaps through a community college?
  3. Will your employer support this (financially or experientially)?

The plan was to respond directly, but the response got long enough that it felt weird to bake it into a reply, so instead, I’m dropping it here. And yes, it’s LONG.

To start, I’ll tackle question #1.

At this point, I won’t ever make money back from that sort of thing. I have 15-25 years before retirement, so I ‘could’ make my money back. However, I’m currently the head of a data management unit, more or less at the top of the pay-scale (if not longevity scale). My responsibilities cover a fairly complex topic (subsistence economies) having a state-wide reach. There is no opportunity here to move up. In theory, there could be, but it’s an avenue that doesn’t match up with my strengths or interest, and again, the pay differential isn’t really that big.

I could remain with my current set of qualifications and skills and do what I’ve been doing: Figuring it out as I go. I woldn’t have any additional financial investment and potentially hit the same finish line. From that perspective, it sounds like I should just not, right? That’s why it’s such a good question.

Practically speaking, I don’t possess the qualifications for the nature of my job even today. My group lives and dies through grants and other non-state government funding. This year, in part because of the pandemic, was an absolute blood-bath. We couldn’t conduct surveys in remote villages, therefore had to push work back and much of our staff had their hours cut, some to 0. It gets more complicated, though. I’ve observed a pretty significant headwind in obtaining this funding. I have no insight whatsoever into why, except to say that the number of funding sources are dwindling. Clearly, we are somehow not competitive enough. With this in mind, I started looking around to see what else might be out there. If we can’t fund ourselves, then I’ll be obliged to do something else. When it comes to being a lead data scientist, pretty much anywhere, the resume needs to include a master’s degree. It turns out, even with 15 years of experience doing this work, I’m not qualified in the eyes of a lot of organizations.

So, that’s a long way of saying: I think expanding my knowledge and capabilities will give me tools to help our team bolster our proposals for funding through analytics or advanced statistical topics. It also gets around to: A lot of the difficulty I had this winter, so far, could be addressed through an increase in staff and project work, and I’d rather stick with this position and the work we do. It’s important.

Question 2 follows question 1. A master’s of data science has a typical price tag of somewhere north of 35,000 for a two-year program. There are less expensive programs that are absolutely world-class, you’ve got to get in first. I could probably obtain a certificate for half of that or maybe even less. So why not that? The short response is that I work in research, and it’s something of an expectation that you’d have an MS, at least and ideally a Ph.D. This expectation also appears in the jobs I was looking at. A certificate is an excellent qualification to get a job, but it’s generally not worth much, if anything, when applied to a grant application or funding proposal. In part, these qualifications tend to focus on specific technologies. Technologies aren’t super relevant for proposals; knowledge of approaches weighs a lot more in that context.

The second part of the question has to do with community college programs – the short answer is that where I live, the university system has recently been gutted to the point that the governor has to remove ALL education requirements from state job postings. The University of Alaska Fairbanks still has a good program for what I’m interested in, but it’s not delivered remotely and isn’t practical for a working person.

The last question revolves around support from my employer. There is no program to help pay. However, the experiential portion is relevant. The programs I’m looking at connect directly to the work I do daily. Two of the programs require a 6-credit professional practice course. I’m near certain that I could pick one of the upcoming projects I’m already slated to work on and use that. I could basically knocks-out a whole semester that way. So, yes, I do get some indirect support.

So there it is: Some of my reasoning for why.

Image by Tumisu from Pixabay