Data-Driven Finance Course

Lesson 1 • Types of Financial Data

Covers structured vs. unstructured data, time-series vs. cross-sectional formats. Establishes vocabulary for all subsequent data work.

Lesson 2 • Data Quality and Integrity

Defines completeness, accuracy, consistency, and timeliness. Students apply quality checks before any analytical step.

Lesson 3 • Primary Financial Data Sources

Maps market, accounting, and macroeconomic data origins. Students evaluate source reliability and access methods.

Lesson 1 • Reshaping and Transforming Data

Covers pivoting, melting, and aggregating financial tables. Prepares data for ratio analysis and time-series modelling.

Lesson 2 • Feature Engineering for Finance

Derives financial ratios, returns, and signals from cleaned data. Creates inputs for statistical and machine learning models.

Lesson 3 • Cleaning Financial Datasets

Addresses missing values, erroneous entries, and format inconsistencies. Builds on data quality concepts from Chapter 1.

Lesson 4 • Merging and Joining Datasets

Teaches inner, outer, left, and right joins across financial tables. Enables multi-source analysis introduced in Chapter 1.

Lesson 1 • Distribution Analysis and Testing

Tests normality and identifies empirical distributions in financial data. Informs model selection in later chapters.

Lesson 2 • Financial Data Visualisation

Builds price charts, return distributions, heatmaps, and scatter plots. Translates numerical findings into communicable visuals.

Lesson 3 • Time-Series Exploration

Identifies trends, seasonality, and autocorrelation in financial series. Prepares students for formal time-series modelling.

Lesson 4 • Correlation and Covariance Analysis

Measures linear relationships between financial variables. Supports portfolio construction and risk factor identification.

Lesson 1 • Logistic Regression for Finance

Models binary financial outcomes such as default and direction prediction. Introduces classification alongside regression.

Lesson 2 • Time-Series Regression Models

Introduces autoregressive and distributed lag models for financial forecasting. Extends EDA time-series concepts from Chapter 3.

Lesson 3 • Model Validation and Overfitting

Applies cross-validation, train-test splits, and regularisation to financial models. Ensures models generalise beyond the training sample.

Lesson 4 • Factor Models and Risk Decomposition

Applies multi-factor regression to decompose asset returns into systematic and idiosyncratic components. Builds on OLS from the prior section.

Lesson 1 • Credit Risk Quantification

Models probability of default, loss given default, and exposure at default. Applies logistic regression from Chapter 4 to credit data.

Lesson 2 • Volatility Estimation Methods

Covers historical, EWMA, and GARCH volatility models. Provides the foundation for all risk metrics in this chapter.

Lesson 3 • Liquidity Risk Metrics

Measures bid-ask spreads, market depth, and funding liquidity gaps. Extends data wrangling skills to order-book and cash-flow data.

Lesson 4 • Value at Risk and Expected Shortfall

Derives parametric, historical, and Monte Carlo VaR and ES. Connects volatility estimates to regulatory and internal risk limits.

Lesson 1 • Model Deployment and Monitoring

Packages models as APIs and monitors prediction drift in production. Closes the loop from model building to operational use.

Lesson 2 • Unsupervised Learning in Finance

Uses clustering and dimensionality reduction to segment assets and factors. Complements supervised prediction with exploratory structure discovery.

Lesson 3 • Forecasting with Neural Networks

Applies feedforward and recurrent networks to price and return prediction. Extends time-series modelling from Chapter 4.

Lesson 4 • Natural Language Processing for Finance

Extracts sentiment and signals from earnings calls, news, and filings. Introduces unstructured data modelling introduced in Chapter 1.

Lesson 1 • Portfolio Performance Attribution

Decomposes portfolio returns into allocation, selection, and interaction effects. Enables data-driven evaluation of investment decisions.

Lesson 2 • Return and Risk Estimation

Estimates expected returns and covariance matrices from historical data. Provides inputs for all optimisation models in this chapter.

Lesson 3 • Mean-Variance Optimisation

Derives the efficient frontier and optimal portfolios under constraints. Applies risk metrics from Chapter 5 to allocation decisions.

Lesson 4 • Alternative Risk-Based Allocations

Covers risk parity, equal risk contribution, and maximum diversification. Addresses mean-variance limitations with robust alternatives.

Lesson 1 • Financial Dashboard Design

Creates interactive dashboards for risk, performance, and portfolio metrics. Applies visualisation principles from Chapter 3 at scale.

Lesson 2 • Strategic Decision Support Systems

Integrates models, dashboards, and governance into decision support infrastructure. Positions data-driven finance as a strategic organisational capability.

Lesson 3 • Building Analytical Workflows

Designs reproducible end-to-end pipelines from data ingestion to insight delivery. Synthesises ETL, modelling, and visualisation skills.

Lesson 4 • Data Governance in Finance

Establishes data ownership, access controls, and audit trails for financial analytics. Ensures compliance with data management standards.