The largest quant
second brain.
One connected knowledge web of quant finance — lessons, links, and structure you can explore and learn from.
Explore the knowledge webSee the knowledge web
Every lesson linked — explore the map before you start.
Building knowledge web…
Hover nodes to explore connections between lessons.
How it works
Learn the curriculum, practise with exercises in every lesson, then build real projects when they launch.
Step 1
Learn
Work through structured lessons across the quant-finance curriculum.
What's Included
- Calculus I
- Probability I
- Statistics I
- Introduction to Python
- Financial Instruments and Markets
- Probability II
- Stochastic Calculus
- Options Pricing
- Linear Algebra I
- Econometrics
- Portfolio Management
- Quantitative Risk Management
Step 2
Practise
Exercises in every lesson turn theory into something you can compute and check.
What's Included
- Exercises in every lesson
- Problem sets tied to what you just learned
- Worked checks and applied calculations
- Reinforce each unit before moving on
Step 3
Build
In progressReal projects to show your experience—apply the curriculum in code once they ship.
What's Included
- Portfolio projects from real-world scenarios
- Apply units in code
- Projects Lab coming later
Table of Contents
Eight units across math, programming, and finance — from calculus to options pricing.
1. Calculus I
- • Functions and Models.4
- • Limits and Continuity.7
- • Differentiation.9
- • Applications of Differentiation.8
- • The Integral.6
- • Techniques of Integration.8
- • Applications of Integration.7
- • Parametric Curves and Polar Coordinates.4
- • Infinite Sequences and Series.10
- • Ordinary Differential Equations.7
2. Probability I
- • Probability Foundations.6
- • Discrete Random Variables.7
- • Continuous Random Variables.8
- • Multivariate and Derived Distributions.7
- • Limit Theorems.6
3. Statistics I
- • Sampling Distributions and the Normal Family.7
- • Statistical Models and Data Reduction.7
- • Point Estimation: MOM, MLE, and Optimality Theory.10
- • Bayesian Estimation and Posterior Inference.6
- • Confidence Intervals and Interval Estimation.5
- • Neyman-Pearson Theory and Uniformly Most Powerful Tests.7
- • Likelihood Ratio Tests, Wilks' Theorem, and Goodness of Fit.6
- • Linear Regression and Analysis of Variance.8
4. Introduction to Python
- • Python Foundations.3
- • Control Flow and Simple Numerical Programs.7
- • Functions and Decomposition.5
- • Built-In Data Structures.6
- • Files, Exceptions, and Modules.5
- • Object-Oriented Programming.5
- • Testing, Debugging, and Style.4
- • Scientific Python Bridge.3
5. Financial Instruments and Markets
- • Markets, Participants, and the No-Arbitrage Principle.5
- • Fixed Income: Bonds, Yields, and Interest Rate Risk.6
- • Credit: Corporate Bonds, Credit Spreads, and the Default Option.3
- • Equities, Money Markets, and FX.4
- • Forwards, Futures, Swaps, and Contracts for Difference.9
- • Commodities and the Cost-of-Carry World.5
- • A First Look at Options: Payoffs, Bounds, and the Binomial Model.7
6. Probability II
- • Random Walks on the Integers.5
- • Discrete-Time Markov Chains.10
- • Branching Processes.3
- • The Poisson Process and Renewal Theory.9
- • Continuous-Time Markov Chains and Queueing Theory.10
7. Stochastic Calculus
- • Discrete Martingales and the Gambling Blueprint.6
- • Brownian Motion: Construction and Properties.9
- • Stochastic Integration and Itô's Formula.11
- • Diffusions, SDEs, and the Feynman-Kac Formula.7
- • Change of Measure, Girsanov, and Risk-Neutral Pricing.9
- • Lévy Processes and Jump Calculus.7
8. Options Pricing
- • Foundations of Computational Option Pricing.12
- • Binomial Trees, Barrier and American Options.7
- • Monte Carlo Methods and Stochastic Volatility.10
- • Path-Dependent Exotics Beyond Barriers.6
- • Index, Currency, Futures and Multi-Asset Options.5
Your quant second brain
starts here.
Explore the web, then learn unit by unit — from probability through portfolios.