Disappointing curriculum – what to do next?

Electrumite

New Member
Last year, I have started an MSc Finance and am a little disappointed with the curriculum. Don't get me wrong, the courses sounded great and very useful in becoming a quant a.k.a. "Quantitative AI data science machine learning technology course" (I'm exaggerating, but you get the point) but they were mostly not rigorous at all.
I am aware that an MFE would've been the better choice but even though I had plenty of math/stats courses in my undergrad, a BA in Econ apparently disqualified me for the programmes that I was interested in. I will graduate next year when I'll be 24.

The question is: What do I do from here? The main problem is that even if I start applying to jobs, it will be difficult to break into the field and even if I would, for some reason, get in, I do not feel adequately trained.

From my point of view, there are 3 options that I have:

  • Apply to a PhD
    • Pros: I learn the skills and might have an advantage for certain roles​
    • Cons: It is hard to get accepted, it takes an insanely long time (5+ years) and I don't know how big or if there is even an advantage for most roles (over people with MFEs)​

  • Apply to MFE
    • Pros: Easier to get in than PhD, good training for later and strong signal​
    • Cons: 2 more years of studying and might seem redundant to an employer to have 2 finance degrees​

  • Apply for Applied Math MSc
    • Pros: Most likely the easiest to get in, learning the necessary skills and strong signal​
    • Cons: Usually takes 2 years and I'm not sure whether I would then have a chance to break in without an MFE or PhD; it is also hard, which means that I expect lower grades than with the other options.​



I know that I will have to decide in the end, I am just curious of whether you have any comments or what you would do if you were in my situation. Thank you.
 
Last edited:

Daniel Duffy

C++ author, trainer
"Quantitative AI data science machine learning technology course"

Which version number/release of this <product>?

The title is tautologous.
 

Electrumite

New Member
"Quantitative AI data science machine learning technology course"

Which version number/release of this <product>?

The title is tautologous.
I deliberately chose an exaggerated title to emphasize my point that most courses sounded very useful but my hopes of learning useful skills and theory about ML, quantitative finance etc. were all but fulfilled. Of course, "Quantitative AI data science machine learning technology course" as a course itself does (to the best of my knowledge) not exist and I apologize if that title is misleading.
 
You should draw connection between the job you want the a set of classes that would aid getting that job. I don't know what school you are in, but there's not reason why, if you don't ask nicely, you can;t sit in on any course you want in any department in the school. I've done this. It's a better solution that the pain of another program or a PhD.

So again, pick a job, backtrack to the right course, find it (or the professor who teach it) at your school, audit a course (or grab a book and the professors email). Many profs are slow to help, but will try if you get stuck learning something on your won, and that's a good motivator.
 

Electrumite

New Member
I understand, work experience is probably more valuable.

However, would it raise questions for employers if I, additional to the Finance degree, get a Quant Finance or Applied Math degree 4-5 years down the road?
 

Onegin

Active Member
C++ Student
A few in my current program already had MFin or MBA before starting; it’s not terrible to have a strong foundation in Corp finance. Almost all of the top programs, except for MIT, lack this element.
It really depends on your goal; to applied Math’s point. If you decide MFE / PhD degree is helpful, all else equal, I’d say do it now while your opportunity costs are lower.

A lot of the screening for quant roles is now in the domain of the brain teaser books / hacker rank. Sell side recruits super early in the academic year, so if you’re not frosty by Sept / Oct it will be challenging to land a Role.
 
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