STEM to Quant

A structured curriculum for physics, mathematics, and engineering graduates entering quantitative finance. Bridge your technical foundation to portfolio management, derivatives, and systematic trading.

6-9 Months Duration

27 Chapters Content

14 Notebooks Lab Exercises

DSF + QPE Certifications

Prerequisites

Linear Algebra

Matrix operations, eigenvalues, decompositions. Standard undergraduate coverage. Used extensively in portfolio optimization and factor models.

Probability & Statistics

Distributions, hypothesis testing, regression. Graduate-level preferred. Foundation for risk measurement and statistical inference.

Programming

Python proficiency (pandas, numpy, matplotlib). R or MATLAB acceptable. All lab exercises use Python notebooks.

Calculus

Multivariate calculus, optimization. Stochastic calculus helpful but not required. Covered in derivatives modules.

Curriculum Timeline

Financial Foundations

Weeks 1-4

Bridge from STEM to finance. Time value of money, financial statement analysis, and corporate valuation fundamentals. Establish the vocabulary and conceptual framework used throughout the profession.

Vol I Ch. 1

Time Value of Money

Vol I Ch. 2

Financial Statements

Vol I Ch. 3

Risk & Return Fundamentals

Valuation & Capital Markets

Weeks 5-10

Core valuation methodologies for equity and debt. CAPM, factor models, and discounted cash flow analysis. This phase leverages your mathematical background for rigorous asset pricing theory.

Vol I Ch. 4

CAPM & Factor Models

Vol I Ch. 5

Equity Valuation

Vol I Ch. 6

Bond Pricing

Vol I Ch. 7

Cost of Capital

Portfolio Construction

Weeks 11-16

Mean-variance optimization, efficient frontier construction, and multi-asset allocation. Your linear algebra skills apply directly. Build portfolios that balance risk and return under constraints.

Vol I Ch. 8

Markowitz Optimization

Vol I Ch. 9

Multi-Asset Portfolios

Vol I Ch. 10

Risk Parity & Alternatives

Derivatives & Risk

Weeks 17-24

Options pricing, Greeks, and hedging strategies. Black-Scholes framework and extensions. Your stochastic calculus foundation (or willingness to learn it) pays dividends here.

Vol II Ch. 17

Options Fundamentals

Vol II Ch. 18

Black-Scholes Framework

Vol II Ch. 19

Exotic Derivatives

Vol II Ch. 20

Hedging Strategies

Systematic & Algorithmic

Weeks 25-32

Market microstructure, algorithmic execution, and systematic trading strategies. This is where your programming skills and quantitative mindset create direct career value.

Vol II Ch. 23

Market Microstructure

Vol II Ch. 24

Algorithmic Trading

Vol II Ch. 22

Enterprise Risk Management

Is This Path Right For You?

Good Fit

Physics, Math, or Engineering degree
Comfortable with mathematical notation and proofs
Programming experience (Python preferred)
Interested in systematic/quantitative approaches
Targeting quant roles: researcher, trader, PM

Consider Other Paths

Limited math background (below calculus)
Prefer discretionary over systematic investing
No programming experience or interest
Looking for quick overview, not deep mastery
Targeting pure corporate finance roles

Career Outcomes

ENTRY LEVEL

Quantitative Analyst

Build pricing models, backtest strategies, and support portfolio managers with data analysis. Entry point at hedge funds, asset managers, and banks.

ENTRY LEVEL

Risk Analyst

Measure and monitor market, credit, and operational risk. Build stress testing frameworks. High demand at all financial institutions.

MID LEVEL

Quantitative Trader

Execute systematic strategies with real capital. Requires demonstrated backtesting and risk management skills. High compensation potential.

SENIOR

Portfolio Manager

Lead systematic strategies at quant funds. Full P&L responsibility. Requires years of experience plus this theoretical foundation.