Models and Decision-Making


We are living in a digital revolution: every day, in our daily lives, we are not only consuming but also generating large amounts of valuable information. This course aims at supporting the development of the upcoming “Data Translators”: leaders and managers who can understand the power and potential of data, and translate it into managerial decisions. With the goal of creating a broader understanding of how these “digital breadcrumbs” can be used to create knowledge, by bridging the gap between the raw data and the ultimate decision making process, the module will be mostly lecture and discussion based. It will cover the basics of current decision-making theory and, through real-world examples of data driven decision-making, explore how data analytics can be used in different contexts. Important notions of Statistics, Big-Data Analytics, Public Policy, and Psychology will be covered, but this is not a hands-on quantitative course. And, as with great power comes great responsibility, ethical aspects of Data Science will be discussed, at every step.
The course specifically aims at:
1)    Providing conceptual tools to improve the decision-making process;
2)    Offering insight and intuition as to which tools to use in each situation;
3)    Improving the decision-making process, at both for profit and not for profit levels;
4)    Challenging students to think beyond a limited scope and understand the impact of their decisions at a societal level.

General characterization





Responsible teacher

Joana Gonçalves de Sá


Weekly - Available soon

Total - Available soon

Teaching language



Available soon


This is a course at the intersection of theory and practice and there will be no required textbook. State of the art papers will be offered to prepare each class. General references in Python and R programming, Statistical Analysis, and Data for Management can be suggested.RESOURCES.

Teaching method

Classes have 3h duration, with each week driven by a specific problem. During the first part of the class , the lecturer will present current theory and knowledge, deconstructing a specific problem. During the second part, students will discuss state of the art papers or case-studies and debate on current issues. At the end of each class, students will be faced with a new problem, to be discussed in the following week. This offers the course a very stimulating and fast paced environment, permanently linking theory to applications and back again.

Evaluation method

Attendance to at least 8 classes is compulsory.

At the end of each week, students are expected to solve a problem or prepare a paper discussion (4 in total), either computationally or as a case write-up. Creativity is encouraged. Students are always free to work together and discuss the problems and papers with each other, but the write- ups must be individual. The final assessment will have two steps: 1) a single problem, that the students are expected to solve using the learned tools and 2) an oral defence of their solution.

The final grade in the MDM course considers:
-    Individual and Group Problem Sets and Paper Discussions (40% - 10% each)
-    Final Exam (40%)
-    Oral Discussion and Class Participation (20%)

Subject matter

In each week we will focus on specific problems in decision-making, from different contexts, and present new analytical tools to tackle them.