Data Science for Product Management: Making Products Count

Course Number: 
Semester and Units: 
Intermittent: 6 units
Course Description: 

Product managers engage in a variety of complex activities critical to product success. These include product requirements gathering, forecasting customer demand, customer segmentation, and 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 preference modeling, time series forecasting, regression, clustering, classification, A/B testing, and analytics for unstructured data including. Along the way, students will learn about practical aspects of applying data science to product management, including such as choosing appropriate metrics for product success.. This course is primarily aimed at students with technical backgrounds who wish to apply their skills to product management. Backgrounds in basic statistics, and some programming experience are required, as the course will includes hands-on exercises in Python to illustrate the concepts