Description and Objectives
This course is designed for master students from quantitative fields such as marketing, economics, statistics and computer science in their last year of study. It prepares students for solving real-world marketing problems using modern quantitative methods and is a good preparation for a machine learning/data science job in marketing or a PhD in quantitative marketing.
To this end, the course first reviews theoretical foundations in marketing, statistics, probability theory and computer science that are required to understand, apply and customize complex statistical models. The course will then focus on formalizing marketing decisions as machine learning problems and equips students with the necessary tools to efficiently implement machine learning models and pipelines. After completing this course participants will be able to judge how modern machine learning methods complement (or even replace) traditional statistical methods for data analysis and decision-making.
The course content complements existing courses in that it reviews the theoretical foundations taught in statistics and computer science programs and then shows how to implement machine learning approaches to important marketing questions.