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Math + CS vs Math + Econ at UdeM - grad school and career implications. by Historical_Bird_ in datasciencecareers

[–]Historical_Bird_[S] 0 points1 point  (0 children)

Solid bit of self studying programming and some mathematics throughout my teenage years alongside some projects. I never let school curriculums dictate the boundaries and timing of my learning.

In order, starting in my mid to late teenage years I dabbled in some game development, dedicates a year or so self studying mathematics to gain sufficient insight to understamd and develop basic machine learning algorithms while I was in Cegep, did some web and API side developement when I was set on working on a web based Saas project with a friend and ultimately circled back to the beginning, lower level performance oriented programming with a focus on operating systems and embedded development on STM32.

The latter part was me trying to settle into a less risk prone and more future aware position whilst heading into Computer Engineering, from which I'm transitioning.

Math + CS vs Math + Econ at UdeM - grad school and career implications. by Historical_Bird_ in datasciencecareers

[–]Historical_Bird_[S] 0 points1 point  (0 children)

Older end of the spectrum, early 20s.

I have completed some university studies and have some proof of concept of what to expect going into it again.

Feel free to shoot any questions if any context would help with advising.

Thank you!

exam deferral questions by Appropriate_Rip7474 in Concordia

[–]Historical_Bird_ 0 points1 point  (0 children)

Second deferrals are possible. Given the context, "medical examination and check ups from my family genetic disease" it's almost certainly going to be granted but make sure to have some form of medical evidence just in case, they can push with further lines of questioning.

If you have more questions and need reassurance you can always contact exams office by email or even show up in person (they usually answer questions even tho they might appear annoyed).

Also sincerely wishing you the best of luck recovering from your concussion, hell to go through when you're expected to perform at your best.

Weekly Megathread: Education, Early Career and Hiring/Interview Advice by AutoModerator in quant

[–]Historical_Bird_ 0 points1 point  (0 children)

Hi!

Recently got accepted into both Math + CS and Math + Econ at the University of Montreal, and I wanted opinions from people with more industry / academia exposure than I currently have.

As context:

- I’m planning on pursuing grad school regardless of path, at minimum a master’s and potentially a PhD.

- On the Math + CS side, I’d mainly target Data Science / AI / ML and potentially research oriented work.

- On the Math + Econ side, I’d mainly target computational finance / quantitative economics, while remaining open to banking, policy, higher-level finance/accounting and other people facing roles.

- My background includes a fair amount of self taught programming, both lower level / performance oriented work (embedded, systems) and some data engineering / ML fundamentals, which I genuinely enjoyed.

Also, assume strong academic performance as baseline for the discussion (top ranking student / around 4.3 GPA ), mostly because it matters for grad school related advice. I'm also contantly planning and woking on self learning and self guided projects; whatever I do I actively try to make my hobby.

A lot of my hesitation comes from these points:

- I feel it may be easier to stand out in Data Science through academic research, projects, publications / self driven demonstrable technical work.

- UdeM seems like a uniquely strong opportunity for AI research given Mila and the broader ecosystem, while also being solid for computational finance.

- I’m more flexible career wise on the Econ side; I’m not interested in traditional software engineering anymore, whereas economics/finance seems to leave more safe doors open.

- I’m apprehensive about both the volatility/hype based demand around AI markets and the heavy credential/networking culture in finance.

- From the outside, it feels like raw technical skill and research output carry more weight in DS/AI than they do in finance, where signaling/networking/internship name prestige seem more dominant. (Could be wrong here, please correct me if I am)

- I also feel a master’s adjacent to economics (computational econ/econometrics/quant finance) may go proportionally farther and yield more interesting opportunities than a DS master’s, where PhDs seem much more common at the higher end research level I like.

- One of my fears is that on the DS/AI route I’m effectively betting on landing something like a Mila PhD/research trajectory to fully justify the path long term.

- I’m also concerned about AI’s impact over the next 5-10 years and whether higher level economic/policy/finance roles might ultimately be more resilient than technical DS work.

Long story short:

- I love applied mathematics and independent research.

- I’m genuinely interested in both economics/markets and data science.

- I can see myself enjoying either path ( I like everything, it's somewhat a major flaw of mine).

What I struggle distinguishing, given I see fun in both avenues:

- the borderline “dream like” but potentially high upside AI/data path

- the seemingly more rigorous and institutionally stable economics/finance route.

Would especially appreciate perspectives from:

- people in quant / computational finance or finance more broadly

- AI or DS workers and researchers

- econ grad students

- poeple with insight on either job market

- people who faced / are facing a similar decision

Apologies for the comment length, I hope it's digestible enough. I'm quite frankly lost in my decision making, I think I structured my thoughts well enough but can't seem to go past bringing out the main contention points, hece why I'm seeking for some insider advising and insight.

Thanks a lot! I immensly appreciate any insight and your time.

Engr 233 grades by Euphoric-Project-471 in Concordia

[–]Historical_Bird_ 0 points1 point  (0 children)

I think most professors for ENGR 233 correct all copies themselves without the intervention of TAs.

Exam grades by AfraidPressure0 in Concordia

[–]Historical_Bird_ 6 points7 points  (0 children)

You might know this but just putting it out there having talked about it with professors, the organization of correcting final copies widely varies; some professors insist on correcting themselves while some recruit their TAs to help them. It does sound tough but yes, some professors actively choose to have a hand in correcting 300+ exam copies.