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Forecasting & Scenario Development with AI

IBM via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Overview

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By the end of this course, you will break a time series into trend, seasonality, and noise, measure forecast quality with metrics like MAE and MAPE, generate AI-assisted forecasts with clear rules for when to override them, and build probability-weighted scenarios and sensitivity tables that frame the range of outcomes leadership must weigh. These are the skills that turn an analyst into a trusted decision-support partner — someone who can answer the executive's "what if?" in the room, not a week later. You will learn to produce forecasts leadership can rely on and to stress-test the assumptions behind them. What makes this course unique is its focus on judgment over computation. You will not write a line of code or need a statistics background; instead, you learn when to trust a method, which drivers actually move the numbers, and how to curate a wide set of AI-generated scenarios down to the short, defensible list executives can act on. Every method is demonstrated on a realistic case company you carry through the program.

Syllabus

Predictive Forecasting Concepts

Learn predictive forecasting for financial planning and analysis (FP&A) from the ground up. This module builds the time-series foundation every analyst needs to produce forecasts the business can trust. Break a time series into its core components — level, trend, seasonality, and noise — and connect business drivers like volume and price to the patterns in your historical data. Compare simple forecasting methods, including naïve, seasonal naïve, moving average, and exponential smoothing, and learn to judge them with forecast accuracy metrics such as MAE, RMSE, and MAPE. Using holdout (out-of-sample) testing against a naïve benchmark, you'll select the method that produces a reliable rolling forecast — the core skill behind demand forecasting, revenue forecasting, and confident, data-driven financial decisions.

AI-Enhanced Forecasting

Learn how FP&A analysts use AI to forecast faster without losing control of the numbers. This module teaches you to write structured AI prompts that generate a baseline forecast, read prediction intervals and confidence ranges in plain business terms, and evaluate AI output against simple statistical benchmarks using forecast accuracy metrics such as MAE, RMSE, and MAPE. You'll learn human-AI collaboration and forecast governance for financial planning and analysis (FP&A): when to accept, question, or override an AI forecast; how to document overrides with reason codes and audit logs; and how to produce a blended, explainable forecast that controllers, auditors, and finance leaders can trust. Ideal for anyone working on AI forecasting, rolling forecasts, demand and revenue forecasting, predictive analytics, and AI-assisted decision support.

Scenario Analysis Basics

Scenario analysis is a core financial planning and analysis (FP&A) skill for decision-making under uncertainty. This module teaches scenario analysis and scenario planning from the ground up: how to build base, best, and worst-case scenarios, how to translate business assumptions into quantitative driver inputs, and how to weight outcomes by probability to calculate an expected value. Learners connect each scenario to strategic risks, contingency plans, and risk registers, and they learn how scenario analysis differs from sensitivity analysis and everyday what-if analysis. It is ideal for finance professionals, analysts, and managers who want practical, beginner-friendly skills in scenario modeling, probability-weighted forecasting, risk management, budgeting and forecasting, and capital-allocation decision support — with no coding or advanced statistics required.

Sensitivity and AI Scenarios

Learn to pinpoint what really moves your numbers and turn uncertainty into decisions leadership can act on. This module teaches sensitivity analysis and scenario planning for financial planning and analysis (FP&A): how sensitivity analysis differs from scenario analysis and from simple what-if changes, how to build one- and two-variable sensitivity tables (data tables) in a spreadsheet or planning tool, and how to rank drivers by impact using tornado-chart thinking. You will also use AI to stress-test assumptions, generate scenario variants, and curate a focused short list of base, best, and worst cases for executive and board review. Build practical skills in driver-based planning, forecasting, decision support, risk analysis, what-if modeling, and AI-assisted scenario generation — no coding required.