Start here to find your learning path
Use this guide to find the best entry point for your journey in Machine Learning for Quantitative Finance
Requirements
To benefit fully from ARPM you should have complete familiarity with
- Linear algebra - matrix/vector notation, trace, determinant, eigenvectors, eigenvalues
- Multivariate calculus - derivatives, integrals, Taylor expansions
- Probability - distributions, sample space, population versus sample
Finance or coding experience is welcome but not required.
Whether you need to brush up on your mathematics or are completely new to coding and finance,
Primers are available
Paths
1. I am a finance professional
Risk/asset manager, quant researcher, or data scientist with a strong mathematical background
You are exactly who ARPM was built for.
Recommended:
- Step 1 of 2: Quant Bootcamp
Immersive 6-day overview of Machine Learning and Quantitative Finance - Step 2 of 2: Certificate
Instructor-supported 5+5-month deep dive in Machine Learning and Quantitative Finance
2. I am a PhD or graduate student
Background in mathematics, statistics, physics, electrical engineering, computer science, economics
You have the foundation needed to succeed in our programs.
Recommended:
- Step 1 of 2: Mathematical Statistics for Finance (free for all, part of the Certificate)
Mathematically intensive – helps you understand the why, not just the how, of advanced statistical techniques - Step 2 of 2: Lab
Comprehensive, self-paced resource covering theory, case studies, and integrated Python code
3. I do not have a strong mathematics background
Non-STEM undergraduate, or other non-quant pathways
ARPM may be too advanced – at least for now: our programs assume the mathematical background outlined above.
Recommended, before joining ARPM:
- MIT OpenCourseWare: Linear Algebra (Gilbert Strang)
- Khan Academy: Calculus
- Khan Academy: Statistics and Probability
Once you are confident with these fundamentals, our advanced Mathematics Primer will quickly consolidate your knowledge and get you ready for your deep dive.
What Every ARPM Path Includes
- Mathematical rigor – not watered-down data science
- Theory + practice – each statistical concept is tied to real-world financial modeling
- Hands-on Python – notebooks, simulations, and visual content
Still not sure?
Contact us – we will help you assess if ARPM is the right fit, and where to begin.