8 Weeks
typical completion time
Online Self-Study
from anywhere
AI Coach
on-demand study support
1 Year Access
revisit e-learning materials
4 Units
finance, Python, maths, DS, ML & AI
$2,000
individual enrollment
The Quant Foundations curriculum is organized into four units covering finance, Python, mathematics, and data science and machine learning. Each unit combines expert video lectures and knowledge checks to help you build strong foundations in quantitative finance.
Finance Fundamentals
Python Programming for Quantitative Finance
Mathematics for Quantitative Finance
Data Science, Machine Learning & AI Foundations
Detailed Unit Breakdown
Four units covering everything from finance fundamentals and Python programming to mathematics and machine learning, giving you a complete, structured foundation.
Unit 1
Finance Fundamentals
Build a practical grounding in financial markets, valuation, bonds, derivatives, options strategies, commodities, and FX.
- Build a solid understanding of financial markets – Understand how economies, central banks, and markets interact.
- Master the essential building blocks of finance – Learn how money works across time and how financial assets are valued.
- Understand key financial securities – Explore bonds and equities, their characteristics, valuation, and measures of risk.
- Explore how derivatives work in practice – Understand futures, forwards, swaps and options, including their use in hedging.
- Understand global currency and commodity markets – Explore FX, commodities, forward rates, and cross-currency relationships.
- 1.1 Macroeconomics and Markets: GDP, inflation, unemployment, central banks, monetary policy, and the financial market ecosystem.
- 1.2 Time Value of Money: Discrete and continuous compounding, present and future value, discount factors, annuities, and interest rates.
- 1.3 Securities and Valuation: Government and corporate bonds, equities, valuation, yields, duration, credit spreads, and financial risk.
- 1.4 Derivatives: Futures, forwards, swaps and options, including valuation, hedging, option strategies, and Greeks.
- 1.5 Foreign Exchange: Commodity markets, spot and forward FX rates, interest rate parity, FX basis, and cross-currency swaps.
Unit 2
Python Programming for Quantitative Finance
Learn the Python foundations needed to write code, work in notebooks, manage projects, and begin analyzing financial data.
- Understand the fundamentals of Python programming – Learn core programming concepts and why Python is widely used.
- Work with Python tools – Understand how Anaconda and Jupyter Notebooks support coding, documentation and analysis.
- Develop essential programming skills – Work with data types, structures, functions, operators, loops, and conditional logic.
- Use Python for financial analysis – Explore libraries that support mathematics, statistics, data handling, and visualization.
- Understand Python project management – Explore Git, GitHub, virtual environments, and package management.
- 2.1 Introduction to Python and Programming Concepts: Programming fundamentals, procedural and object-oriented programming, and Python in quantitative finance.
- 2.2 Setting Up the Environment: Anaconda, Python and Jupyter Notebooks, including writing and running Python code.
- 2.3 Data Structures and Control Flow: Variables, data types, lists, tuples, dictionaries, sets, operators, loops, and conditional statements.
- 2.4 Functions and the Python Standard Library: Functions, modules, mathematical and statistical tools, data manipulation, NumPy, pandas, and matplotlib.
- 2.5 Python Project Setup: Source control, Git, GitHub, collaborative development, virtual environments, conda, pip, and package management.
Unit 3
Mathematics for Quantitative Finance
Develop the mathematical skills used across quant finance, from algebra and calculus to statistics, linear algebra, and optimization.
- Build a strong foundation in financial mathematics – Understand the core mathematical concepts used in quant finance.
- Develop your understanding of calculus – Explore differentiation, integration and optimization, and their financial applications.
- Apply mathematics to financial problems – Use differential equations to understand compounding and financial models.
- Work confidently with vectors and matrices – Understand linear algebra and its applications to financial data.
- Explore the mathematics behind ML – Learn how mathematical techniques underpin modeling and optimization.
- 3.1 Algebra and Functions: Functions, logarithms, exponential functions, growth and decay, compound interest and loan repayments.
- 3.2 Calculus – Differentiation & Integration: Derivatives, integration, optimization, curve analysis, the binomial theorem and financial applications.
- 3.3 Differential Equations: First- and second-order differential equations, continuous compounding, partial differentiation and applications to financial models.
- 3.4 Linear Algebra & Matrix Applications: Vectors, matrices, covariance, correlation, eigenvalues, eigenvectors, PCA and portfolio applications.
- 3.5 Probability & Statistics: Probability, random variables, distributions, descriptive statistics, correlation and financial risk measurement.
- 3.6 Mathematics for Machine Learning: Monte Carlo simulation, Newton-Raphson, gradients, optimization, vectors, matrices and dimensionality reduction.
Unit 4
Data Science, Machine Learning & AI Foundations
Explore the foundations of data science, machine learning, and AI as they apply to financial data, modeling, and decision-making.
- Turn financial data into insights – Learn how to prepare, explore, and interpret data for quantitative analysis.
- Understand how ML models are developed – Explore the workflow from preparing data through to deployment.
- Explore core ML techniques – Understand supervised and unsupervised learning and their applications to financial data.
- Understand the foundations of modern AI – Explore neural networks, deep learning, reinforcement learning, and their applications in finance.
- Discover how AI is changing finance – Understand LLMs, NLP, prompt engineering, and emerging applications in finance.
- 4.1 Data Cleaning & Exploratory Analysis: Missing values, outliers, summary statistics, financial data visualization, and moving averages.
- 4.2 Time Series Analysis: Scaling, standardization, trends, seasonality, stationarity, ACF/PACF, and forecasting approaches.
- 4.3 Feature Selection & Engineering: Moving averages, volatility, macroeconomic data, sentiment, embeddings, and feature-selection techniques.
- 4.4 Machine Learning Workflows: Data collection, preprocessing, feature engineering, model selection, training, validation, evaluation, and deployment.
- 4.5 Supervised Learning: Regression, classification, logistic regression, KNN, SVM, model evaluation, and the bias-variance trade-off.
- 4.6 Unsupervised Learning: K-Means clustering, dimensionality reduction and PCA for identifying patterns in financial data.
- 4.7 Introduction to Deep Learning: Neural networks, backpropagation, RNNs, LSTMs, transformers, foundation models, and fine-tuning.
- 4.8 Introduction to Reinforcement Learning: Markov Decision Processes, Q-learning and reinforcement learning applications in trading and portfolios.
- 4.9 AI in Finance: Generative AI, LLMs, agentic AI, RAG, prompt engineering, ethics, governance, and regulation.
- 4.10 NLP in Finance: Transformer-based NLP, sentiment analysis, financial text, language models, and RAG for financial documents.
By the end of this program, you will be able to
Analyse financial markets
Understand derivatives, bonds, equities, and FX instruments confidently
Code in Python directly
Build financial applications, analyse data, and automate trading strategies
Apply fundamental mathematics
Use calculus, linear algebra, and statistics in quantitative contexts
Build Machine Learning & AI models
Create predictive models for financial forecasting and algorithmic trading
Work with real financial data
Clean, analyse, and extract insights from financial time series
Learn From Industry Professionals
Every unit is taught by experienced practitioners and academics who work in quantitative finance, bringing together real-world expertise and rigorous academic foundations. Each lesson blends theory and hands-on practice, so you build both understanding and applicable skills.
Industry Experience
Professionals with real-world, hands-on experience in quantitative finance
Academic Rigor
Core concepts grounded in academic theory and subject matter expertise
Practical Focus
Real examples and applied techniques drawn from quantitative finance practice
How You’ll Learn
A focused learning experience combining expert instruction, knowledge checks, and AI support to engage, challenge, and reinforce your understanding.
Expert Video Lectures
Instruction blending theory and practice from industry professionals and academics
AI Support
Ask questions, revisit concepts, and reinforce learning throughout the program
Assessment
Regular knowledge checks and a final assessment to consolidate your learning
Frequently Asked Questions
Quant Foundations curriculum is structured around four units that cover the core disciplines that underpin modern quant finance:
- Unit 1: Finance Fundamentals – Markets, bonds, derivatives, options, and foreign exchange (FX)
- Unit 2: Python Programming for Quantitative Finance – From coding basics to financial applications
- Unit 3: Mathematics for Quantitative Finance – Calculus, linear algebra, statistics, and differential equations
- Unit 4: Data Science, Machine Learning & AI Foundations – Time series analysis, machine learning, deep learning, and natural language processing (NLP)
Both. Each unit combines theoretical concepts with hands-on practical examples using real financial data and industry tools. Throughout the program, you’ll apply your learning to real-world scenarios.
Quant Foundations includes assessments at each stage of your learning:
- Knowledge checks: A short set of multiple-choice questions at the end of each lesson, covering that lesson’s content.
- Topic assessments: Multiple-choice questions at the end of each topic, covering all lessons within that topic.
- Unit practice tests: Multiple-choice questions covering all topics within the unit.
- Final Assessment: A comprehensive case study with multiple-choice questions covering material from all four units. Each learner will receive a randomly selected case study. You must pass the final assessment to complete the program and receive the Quant Foundations digital credential.
Hands-on learning
Every unit includes engaging visualizations, and real-world exercises using real financial data and industry tools