Overview

Learn to move beyond dashboards and predictions to recommend business decisions backed by statistical evidence. Start by building time-series forecasts and quantifying their uncertainty. Then estimate whether an intervention caused a change, using causal inference methods that work when experiments aren't possible. Measure what customers value through choice modeling, conjoint analysis, and marketing mix modeling. Finally, bring it together with decision science: structure options with decision trees, update as new evidence arrives with Bayesian methods, and stress-test recommendations with simulation. Across four prescriptive analytics projects, you will forecast revenue, measure a campaign's impact, price a new product tier, and defend a pricing decision to leadership.

Syllabus

Time-Series Forecasting

Learn end-to-end time series forecasting, from preparing data and identifying trends, seasonality, and stationarity to building forecasts with spreadsheets, BI tools, Python, classical statistical models, neural networks, and foundation models. Evaluate forecast accuracy using appropriate validation strategies, error metrics, diagnostics, and prediction intervals while communicating uncertainty to stakeholders. Compare forecasting approaches based on interpretability, scalability, and business requirements, then apply these techniques in a capstone project to recommend a forecasting strategy for real-world decision-making.

Causal Inference

In this course, you will learn how to uncover cause-and-effect relationships in observational data—an essential skill for driving business decisions, policy evaluations, and scientific insights. You’ll explore a powerful suite of causal inference methods designed for time-series and panel data, including Interrupted Time Series, Difference-in-Differences, Event Study, Synthetic Control, and Regression Discontinuity models. Each lesson features hands-on Python exercises to build your technical fluency, and you’ll apply what you’ve learned in a final project that demonstrates your ability to estimate and validate causal effects. By the end of the course, you’ll be equipped to translate complex observational data into actionable insights that support evidence-based decision-making.

Customer Behavior and Analytics

Build practical skills in advanced statistical analysis to solve complex business problems with data. Apply choice modeling, conjoint analysis, causal inference, multidimensional scaling, factor analysis, structural equation modeling, and marketing mix modeling to evaluate customer preferences, estimate treatment effects, uncover market opportunities, and optimize marketing investments. Develop Python implementations using industry-standard libraries, interpret results responsibly, and translate multiple analyses into stakeholder-ready recommendations that communicate uncertainty, justify business decisions, and drive strategic action.

Decision Science and Strategic Modeling

The "Decision Science and Strategic Modeling" course equips learners with essential decision-making frameworks and methodologies. Students will explore the complexities of human decision-making, simulating bias and uncertainty while grasping decision theory fundamentals. Through structured decision tools like decision trees, influence diagrams, and programmatic modeling, learners will transition from theoretical predictions to practical decisions. The course emphasizes Bayesian thinking, sensitivity analysis, and the crucial connection between causal inference and actionable outcomes. Participants will also develop skills in communicating uncertainty, trade-offs, and recommendations effectively, culminating in a real-world pricing decision case study.