20595 - BUSINESS ANALYTICS
Department of Management and Technology
Course taught in English
Go to class group/s: 23
Course Director:
ALFONSO GAMBARDELLA
ALFONSO GAMBARDELLA
Mission & Content Summary
MISSION
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.
CONTENT SUMMARY
- 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)
KNOWLEDGE AND UNDERSTANDING
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.
APPLYING KNOWLEDGE AND UNDERSTANDING
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
- Practical Exercises
- Collaborative Works / Assignments
- Interaction/Gamification
DETAILS
- 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
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ATTENDING STUDENTS
Attending students will be offered two main moments of evaluations: group projects; final exams.
The group projects require the analysis of a case that has to be built by the students from scratch, by devising the problem, to collecting data or running simulations to acquire the necessary information to make the decision. The project will be assessed depending on the richness of the case and the information collected, the analytical level of the exercise, and the clarify of the final report or in-class exposition.
The exam consists of exercises and open questions designed to assess students' ability to apply the analytical tools covered during the course. Specifically, it tests their competence in solving and explaining the approach to strategic decision-making discussed in class, and the tools for running experiments. The exam implies an assessment of how students articulate reasoning, assess the potential effects of a given business practice, and understand the trade-offs involved in organizational decisions..
NOT ATTENDING STUDENTS
Not attending students take a regular written exam with no special assignments
The exam consists of exercises and open questions designed to assess students' ability to apply the analytical tools covered during the course. Specifically, it tests their competence in solving and explaining the approach to strategic decision-making discussed in class, and the tools for running experiments. The exam implies an assessment of how students articulate reasoning, assess the potential effects of a given business practice, and understand the trade-offs involved in organizational decisions..
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
Attending and non-attending students will have to work on slides and readings discussed in class and stored in Blackboard
Last change 24/05/2024 11:31