Excel Data Analysis: Forecasting Course

Lesson 1 • Data Validation and Quality Checks

Applies validation rules and conditional formatting to enforce data integrity. Clean inputs are a prerequisite for accurate forecasting outputs.

Lesson 2 • Navigating the Excel Interface

Covers ribbons, quick-access tools, and keyboard shortcuts essential for speed. Sets the workspace baseline for all subsequent analytical tasks.

Lesson 3 • Core Formula Skills

Introduces arithmetic, logical, and lookup formulas used throughout forecasting. Students build reusable, auditable formula chains.

Lesson 4 • Sorting, Filtering, and Tables

Covers Excel Tables, AutoFilter, and advanced sort options for isolating data subsets. These skills accelerate exploratory analysis before modeling.

Lesson 1 • Descriptive Statistics for Time Series

Applies mean, variance, and autocorrelation measures to characterize series behavior. These statistics inform the choice of forecasting technique.

Lesson 2 • Decomposing a Time Series

Separates a series into trend, seasonal, and residual components using Excel formulas. Decomposition results are reused in exponential smoothing and regression chapters.

Lesson 3 • Visualizing Time Series Patterns

Builds line, column, and combo charts to reveal trends and seasonal cycles visually. Chart interpretation guides model selection in later chapters.

Lesson 4 • Time Series Concepts and Terminology

Defines time series components and explains why temporal order matters. Establishes the vocabulary used in every subsequent forecasting chapter.

Lesson 1 • Double Exponential Smoothing for Trends

Extends smoothing to series with a linear trend using two smoothing parameters. Students apply Holt's method and interpret the level and trend components.

Lesson 2 • Simple Exponential Smoothing

Introduces the alpha parameter and recursive smoothing logic for level-only series. Students optimize alpha using Solver to minimize forecast error.

Lesson 3 • Triple Exponential Smoothing for Seasonality

Adds a seasonal component to handle periodic patterns using Holt-Winters method. Students build the full model and evaluate it against decomposition benchmarks.

Lesson 4 • Weighted Moving Averages

Assigns custom weights to recent observations to reduce lag. Students compare WMA sensitivity against SMA across different datasets.

Lesson 1 • Tracking Signal and Bias Detection

Implements the tracking signal to detect systematic drift in forecast errors over time. Early bias detection prevents compounding errors in rolling forecasts.

Lesson 2 • Model Selection and Benchmarking

Compares multiple models using a structured scorecard and selects the best fit. Students document selection rationale for stakeholder communication.

Lesson 3 • Train-Test Split and Holdout Validation

Applies in-sample and out-of-sample evaluation to prevent overfitting. Students structure Excel workbooks to separate training and validation periods.

Lesson 4 • Key Accuracy Metrics in Excel

Builds MAE, RMSE, and MAPE formulas from scratch and interprets their trade-offs. Each metric is linked to a specific business decision context.

Lesson 1 • Multiple Regression for Causal Forecasting

Adds multiple predictors to capture causal drivers of the forecast variable. Students use the Analysis ToolPak regression output and interpret all coefficients.

Lesson 2 • Time as a Predictor Variable

Encodes time periods as numeric predictors to model linear trends via regression. This bridges smoothing methods and causal regression approaches.

Lesson 3 • Regression Diagnostics and Validation

Tests regression assumptions using residual analysis and influence statistics. Valid models produce reliable out-of-sample forecasts.

Lesson 4 • Simple Linear Regression Foundations

Derives the regression line using LINEST and the Analysis ToolPak. Students interpret slope, intercept, and R-squared in a forecasting context.

Lesson 1 • Sensitivity Analysis with Data Tables

Uses one- and two-variable data tables to map how key inputs drive forecast outputs. Tornado charts visualize which assumptions have the greatest impact.

Lesson 2 • Building Assumption-Driven Forecast Models

Structures forecast models with clearly separated input, calculation, and output zones. Modular design enables rapid assumption changes without formula errors.

Lesson 3 • What-If Analysis Tools Overview

Introduces Goal Seek, data tables, and Scenario Manager as Excel's native what-if suite. Each tool is matched to a specific forecasting question type.

Lesson 4 • Optimistic, Base, and Pessimistic Scenarios

Defines and documents three-scenario frameworks aligned to business planning cycles. Students generate scenario summaries using Scenario Manager reports.

Lesson 1 • Solver-Optimized Smoothing Models

Uses Excel Solver to minimize RMSE by optimizing all smoothing parameters simultaneously. Students constrain parameters to valid ranges and interpret convergence.

Lesson 2 • Nonlinear and Polynomial Trend Fitting

Fits exponential, logarithmic, and polynomial curves to data with nonlinear growth. Students use LOGEST and chart trend-line equations for forecasting.

Lesson 3 • Hierarchical and Aggregated Forecasting

Builds top-down, bottom-up, and middle-out forecast reconciliation in Excel. Students ensure that disaggregated forecasts sum to the aggregate total.

Lesson 4 • Intermittent Demand Forecasting

Addresses sporadic, zero-heavy demand series using Croston's method and variants. Students identify intermittent patterns and apply appropriate Excel formulas.

Lesson 1 • Forecast Confidence Intervals on Charts

Adds upper and lower bound series to charts to visualize forecast uncertainty. Students calculate interval widths from error statistics and format shaded bands.

Lesson 2 • Communicating Forecasts to Stakeholders

Structures written and verbal forecast narratives for executive and operational audiences. Students tailor detail level, terminology, and visual complexity to each audience.

Lesson 3 • Dynamic Charts with Slicers and Timelines

Connects PivotCharts to slicers and timeline controls for interactive filtering. Users explore forecast data without touching underlying formulas.

Lesson 4 • Dashboard Design Principles

Applies layout, color, and hierarchy principles to guide the viewer's attention. Good design reduces misinterpretation of forecast outputs by non-technical audiences.