Course 2023-2024 a.y.


Department of Management and Technology

Course taught in English

Class timetable
Exam timetable
Go to class group/s: 23
DSBA (8 credits - I sem. - OB  |  SECS-P/08)
Course Director:

Classes: 23 (I sem.)

Synchronous Blended: Lessons in synchronous mode in the classroom (for a maximum of one hour per credit in remote mode)

Mission & Content Summary


This course focuses on the area of business analytics concerning decision-making processes. Specifically, it focuses on how to make data-driven decisions in a business context by following a Scientific approach. The course is divided in two parts. The first part discusses the basics of managerial theories such that students learn how to formulate their business strategies and actions using a structured framework. This first theoretical block paves the way for the second part of the course in which the students learn how to apply analytical tools to make data-driven decisions based on their theoretical assumptions. In particular, this part of the course focuses on theoretical and practical aspects of data analysis aimed at finding causal relationships that can be useful in directing managerial action. The overarching goal is to provide the students with an analytical 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 course follows a practical flavor and involves concrete uses of data and real-world examples from leading companies. Attending students will have the chance to engage in a group project, where real managerial problems have to be tackled. The performance in the projects counts as part of the student’s evaluation for the course.


  • Theory of the firm and of managerial action.
  • The use of theory and data to build analytical frameworks to make managerial decisions: the Scientific Approach.
  • The difference between correlation and causality in making managerial decisions.
  • Methods and instruments to test and predict the results of managerial actions:
  • Prediction and Inference: the role of causality
  • The “Econometric” and “Machine Learning” approaches
  • Collecting data: Survey methods and sampling
  • Refresher: Linear Models & Limited Dependent Variables
  • Regularized Regressions for Inference
  • Experimental design and analysis
  • Econometrics of Randomized Experiments
  • The role of cognitive biases in managerial decision-making.
  • Case Studies and Examples from the industry: talks with data-driven startups and companies.
  • Building experiments to make informed managerial decisions.


Intended Learning Outcomes (ILO)


At the end of the course student will be able to...


  • 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 the different approaches of econometrics and machine learning.
  • Learn how to tackle cognitive biases in managerial decision-making.



At the end of the course student will be able to...


  • Master managerial theories to make managerial decisions.
  • Develop business problems as theories in a structured framework.
  • Apply analytical techniques to make data-driven managerial decisions.


Teaching methods

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



  • Lectures.
  • Practical activities: formulation of theories about innovation decisions, and test with actual 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.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
  • Individual assignment (report, exercise, presentation, project work etc.)
  • Group assignment (report, exercise, presentation, project work etc.)
  • Active class participation (virtual, attendance)




Not attending students take a regular written exam with no special assignments 

Teaching materials


  • Lecture slides & handouts
  • Material referenced in the slides & handouts




The material for the preparation of the exam as a non-attending student is the course material listed above for attending students.

Last change 31/05/2023 15:33