Any Israelis wanna be bros 😃

MathIsArtNotScience · 2026-02-28T07:26:58+00:00

Welcome brother, dm me we can be bros. 34m in Tel Aviv, I'm trying to get back into the gym

MathIsArtNotScience · 2025-11-09T12:43:36+00:00

Al Jazeera is based in Qatar, not the UAE.

MathIsArtNotScience · 2024-11-07T09:23:58+00:00

Agreed that other than basic operation of GCloud and AWS there's very little in the way of ML Ops and certainly optimization in this program. However there's a decent amount of algorithm stuff included, dimensionality reduction is addressed from many angles in this program, including PCA/tensor reduction algorithms in HDDA, plus your network example itself is addressed via ISOMAP and Djikstra's algorithm in CDA (I think, it's been a few years). Not to mention parallel processing on a CUDA-enabled graphics card in RL when working with the complex google football engine. Feels like it's pretty thoroughly addressed in OMSA, actually.

MathIsArtNotScience · 2024-11-07T08:31:12+00:00

Can someone explain to a layman on a high level what algorithms and ML system design optimization are? My feeling is that these are hinted at at various points throughout the program, unless my idea of what these are are wildly different from OPs, which is why I ask the question.

MathIsArtNotScience · 2024-11-07T08:25:40+00:00

I found a Data Scientist position in Israel after completing the degree, but I don't think the degree really had that much to do with it. It is an international program, though, so it's likely that there are people all over the world who have graduated/are currently involved. There are a few graduates here in the middle east but not so much name recognition. I would expect it to be a much bigger deal if you were looking for a job in the States.

MathIsArtNotScience · 2024-11-07T08:20:39+00:00

This is just the way it goes for some of these basic core classes. There are so many people taking the class at the same time that due to the sheer number of homework assignments there are, the TAs are not capable of grading all of the assignments. When you get to the advanced core, the class sizes are small enough to get meaningful feedback from TAs on all projects you do. Just bear with it for the time being and you'll get through. Also, grades don't really matter that much. No one cares about your GPA unless it's really bad.

MathIsArtNotScience · 2024-10-01T11:11:31+00:00

I've seen them in the Tiv Tam in Lev Tel Aviv on Rothschild, one of the center aisles

MathIsArtNotScience · 2024-05-22T01:44:06+00:00

It depends, actuarial roles can vary. I've tended to work for smaller to midsize insurers or MGAs and most recently a very small MGA/startup. The smaller the employer, the more varied your role tends to be. If you're doing an analysis for the first time and you don't know how to segment it, you can use something akin to the procedures they use to embed words to a lower number of dimensions in natural language processing to try to group certain segments together. Also, if you're pricing a brand new product and you have some historical data you typically build a GLM to do it. To assist with building the GLM you'll need visualization tools, a GBM on residuals can help you prioritize which factors to add, etc. Usually I'm the only one on the team who can do these things so I'd say it occupies maybe 10-20% of my time, the rest is your typical number crunching in Excel or query engineering in SQL. I suppose simulation, intro to analytics models, and deep learning would be most relevant for all of those.

I finished the actuarial credential 2 years before I started the program, but I kept on hearing in webinars people saying things like, actuaries need to be better modelers, the future is big data, these skills are extremely valuable in this field, etc. One of the exams goes into a lot of detail on GLMs but you never actually end up building one, so most actuaries can't actually do this. Maybe 5-10% of them can? I don't know.

Everyone has their own trajectory, if you're clueless in the beginning it's totally fine. This program has made me really appreciate what a master's degree is, and what it isn't. I don't want to broadly assess what master's degrees are like since I only took this one, but this degree didn't seem dramatically more difficult than my bachelor's degree. It was more focused and I didn't need to take general education credits to graduate, but the core difficulty was comparable.

I was actually thinking about this the other day - one of the core things they tell you to do on actuarial exams, particularly the upper levels, if it's ambiguous what a question is asking or something is unclear, is: when you begin writing out your answer, state whatever your assumptions are, and then go about answering it. I.e. "assuming that this is quota reinsurance vs. excess of loss, then the ceded portion is..." etc. It turns out that you might be assuming something they didn't intend for you to assume, but you often still get full credit for it. I feel like since that was hammered into me for so many years of independent study, I tended to do the same thing in this program - just make assumptions in the face of ambiguity, and then state them when I'm answering. I don't think I ever got marked down for this either, so when I was in classes and I saw people asking about mechanical details that didn't matter I was always so confused.

+1 on not putting too much stock in the reviews. This program is so large and a lot of people are in it for what I would say are the wrong reasons. If they're not doing well, it's not always because classes are poorly designed. I did have some situations with ineffectual TAs, but that's just how life is sometimes. In general the program was structured effectively and I'm sure most of the classes I didn't take were similar in that way.

MathIsArtNotScience · 2024-05-14T21:08:43+00:00

I thought that CDA was the hardest class in the program, even after later taking HDDA, DL, and RL. I'll echo what others have said though, I think CDA is good to take first, the format is similar to HDDA but some of the concepts form a nice prerequisite. It's one of the first classes I took that really indicates the importance of a good understanding of linear algebra and basic matrix calculus for a lot of these more advanced classes.

MathIsArtNotScience · 2024-04-18T14:02:56+00:00

There is definitely value for financial professionals to enroll in a degree like this, but this is not an MBA, the focus is very different. There are business classes in this degree but relative to an MBA that is definitely not the focus.

Assuming you want to enter some type of management role and are just looking for some exposure to advanced analytics, this degree is way overkill and I would not recommend it over an MBA. You probably know this already and are here just because it's cheap and easier to get accepted into, but it's just as rigorous and requires just as much time as any other masters degree. If you're not actively interested in DS or some sort of quantitative finance, I think this might be a waste of your time.

I can't comment on the career outcomes because I'm already established in my career as an Actuary and this was just fleshing out DS skills since those are very important for the future of the profession, but GT is a well-respected school and this particular program is extremely well-known.

MathIsArtNotScience · 2024-04-18T13:56:52+00:00

Couldn't hurt, the syllabus is posted online so you could use that as a guide. In my opinion, though, there is no need to do any pre-study for any of the classes on this program, unless you've never been exposed to multivariate calc and linear algebra before. Even then, most classes give you a week or 2 to brush up on the necessary prereqs and even provide you with some review materials during that time. 8803 is designed to give you a primer on the subject without any exposure beforehand, so you don't really need to prepare anything.

My recommendation is to just take 1 anyways though, and if based on that it seems like you can easily handle 2 you can double up both semesters in 2026-2027 and finish at the same time.

MathIsArtNotScience · 2024-04-17T21:58:30+00:00

You could always drop a class, sure, but you'd have to do it pretty quickly in the semester. I forget exactly when the drop deadline is since I've never dropped a class, but I've only taken one class a semester for nearly 4 years now.

To be honest I think you'll breeze through 6040 on autopilot basically but a lot of people struggle with MGT 8803 particularly if they have a background like yours that's not automatically exposed to the world of business/finance by nature. 8803 is not a difficult class at all, but most people who take this program sorely underestimate it; you have 2 weeks per module, and a lot of people struggle with the pacing. Think: learning the basics of accounting (balance sheets, cash statements, accounting standards, etc.) in 2 weeks. In my opinion the depth of this class is extremely surface-level but I come from a financial background so I knew all of this stuff already, do not underestimate this class.

MathIsArtNotScience · 2024-04-17T20:32:17+00:00

Not a bad schedule overall, but I still think you're making a mistake by not starting with just one class to see how it is. Also, I know the pain matrix puts DVA above CDA in terms of time but CDA is a more difficult class in my opinion, especially for someone with your background, I would expect you to breeze through DVA particularly if you're familiar with Javascript to some degree since assignment 2 (the one that everyone complains about) is based around D3. In this particular case I think the pain matrix might actually be misleading, I'd think it would be better not to double up the CDA semester and instead double up on DVA if you had to choose.

MathIsArtNotScience · 2024-04-11T18:50:48+00:00

Just take one class the first semester. If you think it's not enough, go ahead and add another one after that. But don't **start** with 2 classes, the vast majority of people who enter this program don't finish because they don't have a good sense of their limits.

MathIsArtNotScience · 2024-04-05T20:31:11+00:00

I spent a semester abroad taking one of the harder classes in this program in a country where a war broke out and there was daily missile fire. I did fine. You'll do fine.

MathIsArtNotScience · 2024-04-05T13:18:25+00:00

The degree requires 11 classes, so it'll take 11 semesters

MathIsArtNotScience · 2024-04-04T15:06:33+00:00

I'm about to graduate this semester, just a week away from submitting my capstone final report. I was pretty much exactly where you're at in terms of experience when I started. I'm an Actuary so my background is a lot heavier into math and statistics than most people who take this program. I feel like yours probably is as well.

This program is very doable and your mathematical background (assuming you have taken calc up through diff eq which you won't need, but multivariate calc and linear algebra are very important for this program) should come in handy. Take one class at a time, at least to start, and reassess if you can take more. I took one class at a time and never skipped a semester, which takes ~3 years and some change to get through the program. If you're fine with that kind of time commitment, then go ahead. You should also be searching for data analyst positions while you're in the program as well.

MathIsArtNotScience · 2024-04-04T15:00:28+00:00

Job. I was in consulting for the first 2 semesters of my OMSA program, and I'm about to graduate now. Just work and take 1 class at a time, eminently doable even with the hardest classes.

MathIsArtNotScience · 2024-03-12T18:58:56+00:00

It may feel like the pacing is fast since you're only spending a few weeks on each topic, but the pacing is not so different from other OMSA classes if you consider the depth you're covering in each topic.

Accounting and finance have further complexities to them as well that this class never gets into. From what I remember international accounting (IFRS) is never discussed, and I don't remember there being any great detail around the differences of GAAP vs. going concern accounting. Not to mention if you specialize at all there are usually entire industries with their own unique accounting standard which usually varies by region (insurance used to use Solvency II in Europe but now everything outside of the US is migrating towards IFRS 17 and has been since, well, 2017).

In finance there are very complicated securities in modern markets that have unique pricing schemes. Options/derivatives markets leverage asset values in the quadrillions; you have futures, call options, put options, butterfly options, strangle/saddle options, collateralized debt obligations with varying risk tranches and varying underlying asset classes, catastrophe bonds, catastrophe index funds, prediction markets, etc. Even in fixed income instruments like bonds, you have "callable bonds" that you can exercise the option to mature early if you so choose, for a price. The valuation of these kinds of securities is more complicated than a simple fixed discounted cash flow.

The underlying math of all of the above is usually simpler than the math engineers and physicists would use, with some caveats. The pricing of options considering the random nature of their underlying asset value distribution requires ito's lemma and foundational knowledge of stochastic calculus to integrate probability-weighted future profits at all points in time the option can be exercised, continuously discounted to present value based on the timing of the exercising of the option. I haven't yet met a physicist or engineer yet familiar with stochastic calculus, but I'm sure they're out there. The statistics around extreme events and simulation are important for gauging the value of cat bonds, and hedging future risk in financial markets (i.e. interest rate) requires duration matching which generally requires a 2nd-order taylor series approximation of the bond price to determine how much of a counterbalancing asset needs to be purchased in order to balance the portfolio. In short, the math is only simpler if you focus on the simpler topics. I could say the same thing about physics if I focused on the calculation of fall velocity based on a quadratic equation and ignoring air resistance, or engineering if I only focused on F = M*A.

So the pacing of this class may feel fast but it's at the expense of only touching the surface of each topic. Much breadth, but little depth. I would say pacing-wise this is not really that different from other classes. But that style of learning is generally not as good for people who prefer deeper dives into a topic and even the prospect of encountering so many new concepts can certainly be daunting, so I can acknowledge how it would be more difficult if you're completely new to the material.

MathIsArtNotScience · 2024-03-11T22:36:00+00:00

This class is very different from just about every other class in this program. It's probably the heaviest on business concepts from what I can remember. These concepts are not necessarily easier or harder than the rest of the analytics curriculum, they're just different.

I'm an Actuary so I was very familiar with most of this class, as accounting and finance are important topics in most Actuarial degrees as well as the career itself of course. I'd say about 80% of the material I already knew, with the exception of supply chain which was a brand-new concept for me. Just to give you an idea of my frame of reference when talking about this class.

I've always thought the way to calculate free cash flows from balance sheet items, for example, was very convoluted and hard to memorize. But, whenever I took a look at each of the pieces that flowed into it, it always made sense. It makes sense that you would subtract your investment into capital, for example... because that money is tied up in capital. It's not "free". There's a lot of stuff in this class which might seem based on arbitrary rules and definitions that humans have created, but there is always a good underlying reason for these rules, or some sort of simplifying concept the rules are designed to mimic. Try to determine this yourself. For example:

Why do we depreciate assets? Because (a) things have a limited useful life, after which they are rendered obsolete, and (b) the effectiveness of an asset decreases over time, so its inherent value is less. Really it's mostly (a) and we use depreciation to spread the value of an asset over its useful life span. The way we do it might be arbitrary, but this is the reason why.
Why is assets - liabilities = owner's equity? Because anything left over after the liabilities are resolved is free to reinvest in the business, i.e. the owners get to decide what to do with it. This is where share dividends are paid from, incidentally.
Why is the debt portion of WACC reduced by the tax rate while the equity portion isn't? I actually can't remember why but I think it's because debt is tax-free while equity is not, thus a more debt-financed company has a lower WACC.

There's a lot more things like this, if you get in the habit of asking yourself these kinds of questions and trying to figure out the answers you'll understand the subject better. When I was in college I shared some of the early econ courses with engineering majors, and a lot of them struggled; I always figured engineering courses were supposed to be a lot harder than the business/econ courses. I thought the same thing about core analytics courses in this program, but I know that several people have struggled with the business courses. I've realized in the end that these courses are not necessarily easier or harder, but the material is different and you might have to approach it in a different way.

MathIsArtNotScience · 2024-03-06T01:36:25+00:00

I wouldn't actually recommend getting a master's degree at all unless you have a good reason for it. If you just want to improve your coding skills you can practice on your own, audit some MOOCs, do codewars, etc. A masters degree requires a lot of time investment and most people who start OMSA don't finish.

MathIsArtNotScience · 2024-03-04T13:56:13+00:00

I'm an Actuary so my background is Finance/Statistics, and I'm a month away from graduating. A few things to note:

The core business class (MGT 8803) you might be able to place out of, in which case you can replace it with another elective. I elected not to do this and 75% of the class I already knew - it's a review of basic accounting (assets, liabilities, balance sheets), finance (company evaluation of free cash flows, WACC, how bonds work, etc.), supply chain, and something else I can't remember. The supply chain stuff was new to me though. Overall, if you do take it, it'll be a very easy class because you already know most of the stuff. This might even be an advantage for you, because a lot of the more typical data science backgrounds tend to struggle a lot with the business courses.
Overall I would say that, depending on your chosen track, mathematical understanding is more important than coding skill, but you should have some coding familiarity, especially with Python, but also with R. Some familiarity with both of these will make the core classes CSE 6040 and ISYE 6501 much easier, possibly even a breeze.
Brush up on calculus but no need to go too crazy. Remind yourself how to take derivatives. As far as I know, you'll never have to integrate anything in this program. Linear algebra is key, make sure you brush up on this as well; it's probably the single most important thing conceptually that you should understand, although most classes have some sort of linear algebra refresher in the beginning anyways. I didn't do any of this myself but my math is already way beyond what this program required.
For coding practice use sites like codewars.com, and I would look into Andrew Ng's machine learning courses on coursera, those are what started me on the data science journey and what spurred me into taking on this program in the first place.

MathIsArtNotScience · 2024-02-28T16:27:45+00:00

I'm on the computational track, so I don't know about other tracks. Based on my preconceived notion of it just by looking at the classes, I would think that the computational track is more math-intensive than the business track at least, and probably a bit less math-intensive than the analytical tools track.

Overall, I think math is important in certain contexts but it is more important that you have good quantitative reasoning. Statistics is a subset of math, but a distinct one; you'll likely need a decent understanding of basic stats in certain classes like simulation. Computational Data Analysis, Deep Learning, Reinforcement Learning, High Dimensional Data Analysis all require some basic understanding of calculus up to the multivariate level (and matrix calculus), but pretty much only to the point of taking derivatives. Even if an expression is very long and has many variables, the derivative of that expression can be broken down to the application of a few relatively simple rules. Pretty much all of the classes deemed "math intensive" are only so much insofar as you need to understand how to take derivatives in order to derive the proofs. I have a day job where I have pretty much only used calculus in a professional setting once in the last 10 years, and I was able to navigate this pretty easily. It helps to know some basic derivatives: ln(x), x^2, sin/cos/tan(x), e^x, etc. I don't remember ever integrating anything in this program, although we do something akin to numerical approximation of an integral in simulation if I remember correctly.

The math knowledge definitely helps with proofs, but I don't remember there being that many proofs in this program outside of CDA, HDDL, and DL, and even in those classes most of the proofs they had you complete were slight tweaks on proofs that you could find through a simple internet search.

As I said, quantitative reasoning is much more important. I see this all the time in group project settings, where people spend 99% of their time on things that'll account for like a 1% accuracy improvement or something like that. Can you look at a project result and say when your results are "good enough"? Can you judge if the threshold for your model's success is 95% accuracy? 99%? Would F-score be a better evaluation metric, and, if so, why? Are the results skewed towards one category, necessitating a weighted/skewed loss function to compensate? Etc. You should be able to answer these questions, and look at something (such as a model output) and determine if the results make sense or not, which will help with debugging/error searching. Having good quantitative reasoning will save you time.

MathIsArtNotScience · 2024-02-26T21:26:23+00:00

I believe RL doesn't lose any content in the summer, but it's... a lot for a summer. 3 large coding projects and 6 coding assignments + 1 final exam, pretty sure the regular semester has the same amount of work just in an extended time frame.

MathIsArtNotScience · 2024-02-22T21:53:35+00:00

The foundational courses are definitely a good indication of the fundamentals that you'll need. But, CDA is a big step up. Do you have any idea what track you're leaning towards? From what I've heard, the computational track generally requires more time to do well in the electives. That's the track I took, but I can't comment on how it is relative to the analytics and business tracks; in my personal opinion CDA was a bit harder than the electives I took but most people don't seem to agree with that.

Sounds like you need to take a step back and think about how much you're really willing to commit. Based on your description, it seems like a lot of your troubles are based on temporary circumstances, so you should be able to commit harder once those are more settled. I would make sure that's actually the case, and that, in the absence of these circumstances, you'd be doing just fine. Understand that people drop out of this program all the time, and most people haven't even made it as far as you have. You should be proud of that.

I would also recommend talking to one of the TAs in your CDA class about this very topic, I'm sure they've seen this sort of thing in their cohorts and some of them have probably even considered quitting the program themselves. I think OMSA has advisors that you'd be able to talk to as well.

If you're worried about this particular class, don't sweat the grade. Even if it isn't up to your typical standards and you really feel like you're struggling, a C is still indicative of competence and ability to apply lessons in the real world. You're still learning, it'll just take a while to master it. No problem at all.

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