Introduction to Spreadsheets and Models Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Spreadsheets: A Tool for Thinking with Numbers

Estimated time: 1 hour

• Introduction to the history and basic capabilities of spreadsheets
• Understanding different types of data and spreadsheet notations
• Learning common built-in formulas and functions
• Using conditional expressions and understanding relative and absolute references
• Identifying and correcting common errors in spreadsheets

Module 2: From Spreadsheet to Model

Estimated time: 1 hour

• Structuring a spreadsheet to model variables, objectives, and objective functions
• Constructing simple cashflow models
• Conducting what-if analysis and sensitivity analysis
• Understanding the limitations of simple, deterministic models

Module 3: Addressing Uncertainty and Probability in Models

Estimated time: 1 hour

• Understanding random variables and probability distributions
• Applying power, exponential, and logarithmic functions in model formulas
• Creating probability trees and decision trees
• Using regression tools to make predictions

Module 4: Simulation and Optimization

Estimated time: 1 hour

• Implementing Monte Carlo simulations in spreadsheets
• Using linear programming for optimization
• Utilizing Excel's Solver to optimize resources
• Identifying the similarities and differences between Excel and Sheets

Module 5: Final Project

Estimated time: 2 hours

• Develop a comprehensive spreadsheet model incorporating variables, objectives, and functions
• Perform sensitivity and what-if analysis on model inputs
• Apply optimization techniques using Solver to improve decision outcomes

Prerequisites

• Familiarity with basic computer operations
• No prior knowledge of spreadsheets required
• Access to Microsoft Excel or Google Sheets

What You'll Be Able to Do After

• Build and structure effective spreadsheet models for business analysis
• Apply essential formulas, functions, and references in spreadsheets
• Conduct sensitivity and what-if analysis to support decision-making
• Use optimization tools like Solver to solve resource allocation problems
• Incorporate uncertainty and probability into models for realistic forecasting