Is a M.S. In Math worth anything?
I'm currently in my first semester in a top 20 MSc in Computer Science program. I'm struggling a lot in CS courses and don't find them interesting. Personally, I find math (discrete, calculus, geometry) to be more enjoyable and so, I'm thinking of switching to a MSc in Math.
My question is: is a MSc in Math marketable to IBs and HFs? Are you likely to be hired by BBs with this degree or is a MFE or PhD necessary?
If your school is a target, then you should be fine with the math degree. The CS degree is also fine, but it's more likely to place you into a software development role.
You will be at a disadvantage vs. the MFE kids, so you should brush up on your knowledge of the financial markets.
I would not take an MS in math lightly. You may find geometry and calculus easy but you will learn things far more difficult to grasp in such a program. No MS will come easy; you must struggle in order to learn.
A M.S. in Pure Mathematics is potentially more 'impressive' than an MFE depending on the school. If you can learn the C++ and Finance on your own then an MS will get you the same interview opps that an MFE would.
M.S. Math is not a joke. Did a BA in pure math at an Ivy... it wouldn't help much overall for recruiting, the interviewer could just be really impressed by it though.
My current school is NYU. I hear their math dept is pretty strong, so hopefully a MS in Math there will be attractive to BBs. It also leaves the door open for a PhD if I'm feeling particularly masochistic.
I will have algorithms/data structures and a programming languages course before switching to math. I just don't want to be seen as any kind of expert at algorithms and programming.
You will not have a hard time getting quant interviews coming out of Courant. That is an absolute fact.
Courant isa beast
That's good to know. Yesterday, I cracked open a graduate real analysis textbook they're using in the math dept and learned the Monotone Convergence Theorem and its application to infinite / harmonic series on a 45 minute bus ride home (though needed more time to understand the proof). This is all without having finished the prerequisites for the course. Maybe this is an easy theorem to pick up? Or maybe it's time to switch...
Yeah the MCT is pretty easy.
Hm still gonna switch. I tried to learn Quicksort and failed after 3 hours of looking at the textbook, reading websites and watching youtube videos. It's unnatural to suck this much at algorithms...
What is considered a "tough" topic in real analysis?
A tough topic in Real analysis is hard to state because you have to build your way up to everything. But look at something like the cauchy schwarz inequality, a first test would be your ability to prove that inequality holds.
I'm not trying to be rude but it seems odd that a good mathematician would have any trouble with a theory heavy comp sci course like algorithms. I mean the whole subject is essentially coming from discrete math and Abstract Algebra.
Hopefully you plan on studying Stochastic Calc and probability theory.
I think what happens is that I get lost in the code and can't keep track of what's been assigned to what, what has changed during the loop, or what the new value is after the program skipped a previous loop because certain conditions weren't met. Algorithms are not conceptually difficult; how a heapsort or mergesort works is straightforward. But it's keeping track of all the changes when you read through many lines of code that's the painful part. I don't have a good memory and I forget where I was. Some of my classmates seem to know exactly where they are after a hundred lines of code. Discrete math is different in that there isn't anything to track (eg, "prove for all n member of positive integers that n^2 >= n").
By contrast, I have no problems with time complexity and big O notation.
I wonder if the ability to keep track of where you are after many lines of complicated code is what separates an average from a great programmer if both have good logical / reasoning ability.
What if I told you this is precisely what occurs in the math department. You think ability to prove something like that Cauchy Schwarz inequality on an exam has nothing to do with memory?
You have the same issues in mathematics. When you attempt to prove things you are essentially parsing through your memory for what will work in this situation. What makes math even more difficult than the computer science course is the fact that this proof is not laid out for you in any sense if your professor is really testing you. You may be able to reason for hours on end that your solution works right up to the end before seeing "that concept of continuity that I thought would give me this property actually is not strong enough on its own". There goes 4 hours.
Well if you had strong memory, you would have worked all the way through your solution in your head. You would have seen before writing anything that you won't get anywhere with this definition and these theorems. The math department is filled with 1)people with good memory 2) People with great intuition. I feel the memory dies hard anyway and intuition can be built so you should attempt to build it up. But please don't make the mistake of thinking you're ready because you enjoyed calculus.
Programming requires a different set of skills from mathematics and I'd argue that someone who is strong in one is often weak in the other. There is plenty of anecdotal evidence out there that good mathematicians are rarely good coders and vice versa.
Right now I go to NYU, aced my graduate discrete math course last semester, am taking multivariable calc and linear algebra right now and will start on mathematical analysis next spring. Guess I'll find out soon whether or not I am ready.
I'm curious as to what school you go to. From my understanding, most top schools don't offer a terminal masters degree in math, they only have phD programs.
Sorry, just saw that you attend NYU.
I'm sorry but I find it hard to believe that good mathematicians are rarely good coders.......whatever anecdotal evidence you have is refuted by my daily life. I've talked to many mathematicians. I've met a lot that may not have been fond of programming but that's because they thought it was a trivial task.
You're looking to jump out of a frying pan and into a fire. I'm assuring you as someone who has studied both subjects fairly intensely that these subjects go hand in hand from the academic approach.
Mathematics undergoes one of the most intense transformations just after the classes you describe. Even an undergrad analysis course may not be intensely rigorous. But just wait until you're in a graduate real analysis class with math graduate students fighting to get into top Phd's or win the respect of their professors. Read this post again on that day. You'll have to prove your worth eventually, trust me on that.
The math analysis course I am planning on taking is the first of the 4 core courses required for the MS Math degree. I guess I'll know the difficulty when I get there. People were cautioning me about the difficulty of a MSCS before I enrolled too. I find all this fearmongering kind of pointless. Why discourage people from studying something tough? If they fail they will figure out how hard it is, pick themselves up and try again. I find Western thinking to be very fatalist when it comes to academics--people believe that academic ability is predetermined by innate aptitude and that some subjects are impossibly difficult to learn. I'm Asian and the Asian attitude is "whatever it is if I want it I can get it if I work my butt off". Then you work like hell and get that degree whatever it is. I know lots of Asians, this is how they get their math, engineering, physics PhDs.
The difference is the Asian wouldn't be trying to find the field he likes the most or that meshes best with his personality. He'd put his head down and do the work.
you're the one who just said he couldn't finish an MS in comp sci......................
Maybe, but this type of thinking is pervasive on Wall Street. People in finance are often less concerned with a perfect vocational fit and more with how much they money can earn.
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