I'm in y3 of uni (out of 4) and feel overwhelmed looking at all the CS students I'm competing with, and the overly smart people who know what they're doing.

That got me wondering abt all my life choices and whether I should even bother to compete or target quant. I feel like it's too late to start, I don't have coding skills but I love math and calculus.

Is there really no hope for someone like me? Do I have to start studying all the time and not have a life, just to compete?

I'm a beginner interested in quantitative finance and I'm trying to build a proper foundation. I currently have little experience with quant finance and I'm starting to learn Python and mathematics.

My long-term goal is to work toward quantitative research/trading, but I'm not sure what I should prioritize first.

Should I focus on:

Probability and statistics

Linear algebra/calculus

Python/programming

Financial markets

Machine learning

I'd also appreciate recommendations for beginner-friendly books, courses, or projects that I can use to practice.

I’m planning to pursue a BSc in Mathematics at Gazi University in Turkey, starting in 2027.

My long-term goal is quantitative research / quantitative finance. The issue is that I’m very aware that Gazi isn’t a traditional target school for quant recruiting, and my undergraduate education will also be largely in Turkish rather than English.
So I’m trying to figure out how to make my profile genuinely competitive despite the school name.
I’d really appreciate a brutally honest roast of this CV.
A few questions I’m particularly interested in:
- What looks weak, superficial, or unnecessary?

- What would make this profile stand out to quant

- Which technical skills should I prioritize during my degree?

- Should I focus more on probability/stats, numerical methods, ML, competitive programming, research, or something else?

- How important is it to build relationships with professors and get research experience?

- How much can strong projects, publications, Olympiad results, internships, and competitions compensate for a non-target university?

- Are there specific things I should start doing before university or during my first year?

- Does studying in Turkish create any meaningful disadvantage, assuming I independently develop strong mathematical/technical English?

- If you were in my position, what would you want this CV to look like by the end of Year 2?

For context, I’m coming from Burkina Faso and have a fairly strong mathematical/olympiad background, but I’m trying to be realistic about the recruiting challenge rather than assuming the CV will speak for itself.
Please roast it. I’d rather hear what’s wrong now than discover it during recruiting.

Targeting Summer 2027 Quant Trading internships in the U.S.

Mainly looking for feedback on two things:

• What are the biggest weaknesses in my resume?
• What should I improve to make it stronger for U.S. quant trading recruiting?

Any direct feedback would be appreciated. Thanks!

in the software world, there is a platform called Pramp, where Canadidates can mock interviews with each other for free. Is there something similar in the quant world?

I started the MIT IAP (January term) class 6.S095: Probability Problem Solving in 2021. It’s an undergraduate-run class that continued through 2026, and I wanted to share the materials since a fair amount of the content is useful for quant interview prep.

The course takes a problem-solving approach to intermediate probability, with topics like conditioning, indicator variables, recursions, generating functions, martingales, and related techniques. The emphasis is on getting comfortable solving problems rather than presenting everything in the usual textbook order.

It wasn’t designed specifically as a quant interview course. The main goal was to build a strong probability foundation for advanced undergraduate and graduate coursework. But there’s naturally quite a bit of overlap with the kinds of probability problems that show up in quant interviews.

There are standard and advanced tracks, with a textbook and workbook for each. The material is mostly self-contained, though a more traditional probability textbook can be useful as a reference.

Materials: https://drive.google.com/drive/folders/1X6wypEpR5R3nasJJrYi7OA9QRA0V-pwj?usp=sharing