Financial Modeling Fundamentals

Overview

This course introduces the principles and practice of building structured, assumption-driven financial models in Microsoft Excel. You will apply Excel modeling best practices to design clear input, calculation, and output sections; write robust formulas; and document assumptions for transparency and auditability. Starting with model layout and dynamic functions such as INDEX/MATCH and IFERROR, you will develop driver-based forecasts for revenue, expenses, capital expenditures, and working capital. You will then integrate these projections into a fully linked three-statement financial model—connecting the income statement, balance sheet, and cash flow statement with automated logic and validation checks. The course also covers scenario analysis, sensitivity testing using data tables, and model error-checking to support sound financial decision-making. By the end, you will be able to build dynamic, assumption-driven Excel models suitable for business planning, valuation, and performance analysis.

Syllabus

Excel Modeling Techniques and Best Practices

This module covers foundational skills in building structured Excel models. Learners will be introduced to proper layout principles, naming conventions, formula design, and basic troubleshooting tools. The module also focuses on model usability and documentation — essential skills for professional modelers.

Operating Model Forecasting

This module focuses on building forward-looking forecasts for a company’s operations. Learners will develop revenue and cost projections using driver-based assumptions. Lessons will include forecasting techniques for SG&A, cost of goods sold (COGS), depreciation, and working capital. The goal is to build a dynamic, assumption-driven operating model that feeds directly into financial statements and scenario analysis.

Three-Statement Model Construction and Scenarios

This module teaches students how to link the income statement, balance sheet, and cash flow statement into an integrated three-statement model. Learners will create dynamic linking logic using forecasted assumptions and formulas, build checks and balances to validate model accuracy, and then explore scenario and sensitivity analysis. The goal is to create a fully functional model capable of supporting real-world decision-making.