Master of Professional Studies in Product ManagementAcademics > Course

Data Analysis and Decision Making

Understand decision making under uncertainty for products, portfolios, and programs across various industries and environments.




12 Weeks


8-10 Hours per Week

Today’s product managers must have a comprehensive understanding of making decisions under uncertainty for products, portfolios, and programs across various industries and environments. This course focuses on the use of Bayesian methods for informing decisions on products and programs when directing experiments. Examines the testing of product ideas throughout the lifecycle, from customer discovery, to product discovery, to product design and optimization, to channel testing and marketing for growth.

What You'll Learn

Who Will Benefit

Course Topics

Module 1: User Data

Goals, KPIs and metrics, product management metrics, and improving the product.

Module 2: Qualitative Data

Comment surveys, customer feedback, interviews, and forensics.

Module 3: Quantitative Data

Developing a product hypothesis and user personas, demographics, behaviors, online reviews, and Net Promoter Scores (NPS).

Module 4: Sampling and Surveys

User research, asking questions, and statistics of surveys.

Module 5: Experiments and Hypothesis Testing

Lean analytics cycle, Bayes Factors, A/B testing, multifactor testing, classical significance tests and effect size, linear models and regression, and logistic regression.

Module 6: Product Data

Types of product data, user flows, meta data, bounce rates, abandonment and adoption rates, and innovation accounting.

Module 7: Market Research

Competitor analysis, brand positioning analysis, consumer insights, and User segmentation.

Module 8: Document Engineering

Information and systems analysis, business process analysis, business informatics, and database theory and management.

Module 9: Team and Operational Analytics

Flow metrics, team happiness, team velocity, burndown, cycle time, ROI, defect rates, resource capacity utilization, and queuing theory.

Module 10: Big Data Analytics

Machine learning, natural language processing, nearest neighbor analysis, and proforecasting.

Learning Experience


Asynchronous Lectures

Coaching and Mentoring

Live Office Hours

Peer Interactions and Networking

Project-Based Learning

Real-World Assignments


Gregory B. Baecher
Center for Risk and Reliability
Civil and Environmental Engineering
University of Maryland

Gregory B. Baecher is Glenn L Martin Institute Professor of Engineering at the University of Maryland. He holds a BSCE from UC Berkeley and a PhD in civil engineering from MIT. He is the author of four books on risk, safety, and the protection of civil infrastructure, and 200+ technical publications. He is recipient of the USACE Commander's Award for Public Service, the Panamanian National Award for Science and Technology Innovation, and is a member of the US National Academy of Engineering. Dr. Baecher consults to government and industry on project risk management related to civil infrastructure.