Start here: Find Your ARPM Learning Path
ARPM is built for professionals and graduate students with strong mathematical foundations.
Use this guide to find the best entry point for your learning journey.
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
To brush up on those topics and beyond, an advanced primer is available.
Finance or coding experience is welcome but not required.
Both are developed during the ARPM experience. If you are new to either, 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 3: Quant Bootcamp (free with the Quant Marathon)
Immersive 6-day overview of Advanced Data Science and Quantitative Finance - Step 2 of 3: Quant Marathon
Instructor-supported 12-month deep dive in Advanced Data Science and Quantitative Finance - Step 3 of 3: Certification (free with the Quant Marathon)
Validate your skills with a formal, industry-recognized credential
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 3: Mathematical Statistics for Finance (free for all, part of the Quant Marathon)
Mathematically intensive – helps you understand the why, not just the how, of advanced statistical techniques - Step 2 of 3: Lab
Comprehensive, self-paced resource covering theory, case studies, and integrated Python code - Step 3 of 3: Certification (free with the Lab)
Validate your mastery with an industry-recognized credential
3. I do not have a strong mathematics background
MBA, CFA, FRM, Finance PhD, Coding bootcamp, 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: Statistics and Probability
- Khan Academy: Linear
Once you are confident with these fundamentals, come back and take the next step.
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.