Financial Analytics Course

Lesson 1 • Core Financial Statements Overview

Introduces the income statement, balance sheet, and cash flow statement. Connects each statement's structure to the analytical questions it answers.

Lesson 2 • Data Types and Sources in Finance

Distinguishes structured from unstructured financial data and maps common data sources. Prepares students to locate and evaluate raw inputs for analysis.

Lesson 3 • The Financial Analytics Landscape

Defines financial analytics, its scope, and its role in decision-making. Establishes the vocabulary and mental models used throughout the course.

Lesson 4 • Essential Financial Metrics

Covers profitability, liquidity, leverage, and efficiency ratios. Provides the measurement toolkit applied in every subsequent analytical chapter.

Lesson 1 • Sourcing and Importing Financial Data

Covers methods for extracting data from databases, APIs, and spreadsheets. Establishes best practices for data ingestion that prevent downstream errors.

Lesson 2 • Data Governance and Auditability

Establishes controls for data lineage, version control, and access management. Ensures analytical outputs can be traced, audited, and reproduced.

Lesson 3 • Building Analytical Data Models

Introduces relational data modeling and dimensional schemas for financial reporting. Connects data architecture decisions to analytical speed and accuracy.

Lesson 4 • Data Cleaning and Validation

Addresses missing values, outliers, duplicates, and format inconsistencies in financial data. Ensures datasets meet the accuracy standards required for financial decisions.

Lesson 1 • Correlation and Regression Analysis

Teaches linear and multiple regression for identifying relationships in financial data. Enables analysts to quantify drivers of revenue, cost, and performance.

Lesson 2 • Probability and Risk Fundamentals

Introduces probability distributions, expected value, and basic risk concepts. Connects probabilistic thinking to financial uncertainty and scenario analysis.

Lesson 3 • Hypothesis Testing for Financial Decisions

Applies t-tests, chi-square tests, and ANOVA to financial comparisons. Equips analysts to validate findings and communicate statistical significance.

Lesson 4 • Descriptive Statistics in Finance

Covers measures of central tendency, dispersion, and distribution shape for financial variables. Provides the statistical baseline for all subsequent modeling work.

Lesson 1 • Cash Flow and Working Capital Forecasting

Projects operating cash flows and working capital requirements over planning horizons. Links balance sheet dynamics to liquidity and funding needs.

Lesson 2 • Forecasting Techniques and Accuracy

Compares regression-based, time-series, and judgmental forecasting methods. Introduces error metrics to evaluate and improve forecast performance.

Lesson 3 • Financial Model Architecture

Establishes principles of model structure, assumption documentation, and error prevention. Creates a disciplined modeling framework applied throughout the chapter.

Lesson 4 • Scenario and Sensitivity Analysis

Applies scenario planning and sensitivity tables to quantify model uncertainty. Enables decision-makers to understand the range of possible financial outcomes.

Lesson 1 • Discounted Cash Flow Valuation

Builds a full DCF model from projected free cash flows and terminal value. Connects forecasting outputs from the prior chapter to intrinsic value estimates.

Lesson 2 • Valuation Synthesis and Judgment

Reconciles outputs from multiple valuation methods into a defensible range. Develops the analytical judgment needed to communicate and defend valuations.

Lesson 3 • Time Value of Money Applications

Covers discounting, compounding, and annuity calculations as valuation building blocks. Ensures fluency in the mechanics underlying every discounted cash flow model.

Lesson 4 • Precedent Transaction Analysis

Uses historical deal multiples to estimate acquisition value and control premiums. Extends relative valuation to merger and acquisition contexts.

Lesson 1 • Stress Testing and Scenario Risk

Designs adverse scenarios and applies them to financial models to assess resilience. Bridges risk measurement to strategic planning and capital adequacy assessment.

Lesson 2 • Value at Risk and Expected Shortfall

Builds parametric, historical, and Monte Carlo VaR models and introduces expected shortfall. Equips analysts to quantify tail risk for portfolios and positions.

Lesson 3 • Credit Risk Modeling

Applies probability of default, loss given default, and exposure at default frameworks. Connects credit analytics to lending, counterparty, and portfolio risk management.

Lesson 4 • Volatility and Market Risk Metrics

Covers historical volatility, beta, and correlation-based market risk measures. Provides the quantitative tools for assessing price and return variability.

Lesson 1 • Automated Reporting and Distribution

Builds scheduled, automated financial reports using scripting and BI tools. Reduces manual effort and ensures consistent, timely delivery of performance insights.

Lesson 2 • Performance Measurement Frameworks

Introduces balanced scorecards, OKRs, and financial KPI hierarchies. Aligns measurement design with organizational strategy and decision-making needs.

Lesson 3 • Variance Analysis and Diagnostics

Decomposes budget-to-actual variances into price, volume, and mix effects. Develops the diagnostic skills needed to explain financial performance deviations.

Lesson 4 • Return and Profitability Analytics

Analyzes return on equity, return on invested capital, and economic profit. Connects capital allocation decisions to shareholder value creation.

Lesson 1 • Machine Learning in Financial Analytics

Introduces supervised and unsupervised learning techniques applied to financial prediction and segmentation. Connects ML outputs to interpretable business decisions.

Lesson 2 • Optimization in Financial Decisions

Applies linear and nonlinear optimization to capital allocation, portfolio construction, and budgeting. Translates constrained optimization into actionable financial recommendations.

Lesson 3 • Monte Carlo Simulation for Finance

Builds simulation models to quantify uncertainty in valuations, cash flows, and risk. Extends scenario analysis from Chapter 4 to probabilistic outcome distributions.

Lesson 4 • Strategic Decision Support Systems

Integrates analytics outputs into structured decision frameworks for executive use. Develops the ability to translate complex models into clear strategic recommendations.