95-451 Making Products Count: Data Science for Product Managers
6 units, Carnegie Mellon University
Product managers engage in a variety of complex activities critical to product success including Gathering product requirements Prioritizing features Forecasting customer demand Customer segmentation Pricing Identifying buying patterns Analyzing and responding to customer feedback Historically decisions in these areas have often relied on intuition and guesswork, leading to misjudgment of the market and other key factors, and ultimately, product failures. Developments in data science, combining the increasing availability of data from internal and external sources with new algorithms that exploit that data at scale, offer new possibilities for putting product management decisions on a more quantitative and rigorous footing. Students in this course will be introduced to a variety of data science techniques applicable to activities to which product managers typically contribute. These techniques include clustering, classification, A/B testing, and analytics for unstructured data, including clickstreams, text, speech, and images.
Prerequisites: none listed
Offered: Spring 2027, Fall 2026, Spring 2026, Fall 2025
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