Data Analysis and Decision Making
Understand decision making under uncertainty for products, portfolios, and programs across various industries and environments.
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
- Develop survey methods and evaluation models for better development of data in qualitative assessments and how to integrate with quantitative data sets for customer and product discovery.
- Develop demand forecasting and simulation models that leverage both small and big data techniques to develop scenarios for product and program planning.
- Understand the best method choice for evaluating user experience, customer experiences, marketing channel selection, and team operational health and analytics.
Who Will Benefit
- Aspiring product managers interested in learning the key decision making frameworks, scenarios, and methods for product discovery and marketing.
- Active product managers who want to understand the best methods, how to improve surveys, product A/B testing, multivariate testing, and lean analytics.
- Agile coaches who want to instill an experimental mindset and culture of learning through leveraging expert judgment and decision making techniques.
- Educators, consultants, and organizational leads desiring to improve their capacity to teach, advise, or empower their constituencies to effectively lead innovation and drive growth across emerging and established markets.
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.
Coaching and Mentoring
Live Office Hours
Peer Interactions and Networking
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.