20595 - BUSINESS ANALYTICS

This course focuses on two parts. The first part discusses the basics of managerial theories such that students learn how management and managerial tools help managing companies, formulate their strategies and actions. This paves the way for the second part of the course in which the students learn how to make theory-based and data-driven managerial decisions. Specifically, this part of the course focuses on how and when to make these decisions, under what conditions, what are the tools to make these decisions in the most informed way, and the use of data to make them. The overarching goal is to provide the students with an analytic framework to make decisions like investment decisions, the launch of an innovation, the creation of a start-up. The approach can be employed both in smaller firms and start-ups, or larger companies. The approach is very practical and it involves concrete uses of data to make managerial decisions, as well as the realization of concrete projects in class by groups of students about real managerial problems. The performance in the projects counts as part of the student’s evaluation for the course.

• Theory of the firm.
• The use of theory and data to build analytical frameworks to make managerial decisions.
• Methods and instruments to test and predict the results of managerial actions.
• Understanding the difference between correlation causality in making managerial decisions.
• Application of technique to nail down causal relations to make managerial decisions.
• Building experiments to make informed managerial decisions.
• Use of textual and machine learning tehniques to make managerial decisions.

• Learn about the most important theories in management, and their application to practical managerial problems and contexts.
• Learn how to use theory and data to build analytical frameworks to make practical managerial decisions.
• Learn methods and instruments to test and predict the results of managerial actions, and make the underlying managerial decisions in more informed ways.
• Learn to nail down causal relations to make managerial decisions, and build experiments to make such decisions.
• Learn textual and machine learning tehniques to make managerial decisions.

• Master managerial theories to make managerial decisions.
• Develop theories and use data to make data-driven managerial decisions.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Group assignments
• Interactive class activities (role playing, business game, simulation, online forum, instant polls)

• Lectures.
• Practical activities: formulation of theories about innovation decisions, and test with actua data using relevant software.
• Class project developed by groups of students and discussed at different points in time in class with the instructor and the other students.

• Group project: 60%
• Final exam: 35%
• Class participation: 5%   

An attending student is a student who participated in no less than 25 classes. Class attendance is strongly encouraged.

Only written final exam.

Lecture slides and references about relevant material therein.