The curriculum in Data Science and Artificial Intelligence for Economy and Society trains graduates who are experts in the application of data science and artificial intelligence methods to the economic and social sphere. They will acquire statistical-modeling skills, machine learning and artificial intelligence skills, computational skills in intensive computing and advanced programming, and knowledge related to the application of these techniques to the economic and social context.
| Courses | ECTS | |
|---|---|---|
| I year (60 ECTS) | ||
| I semester | ||
| Statistical Methods | 9 | |
| High Performance and Cloud Computing (+) | 9 | |
| One course from Core Group A (+) | 6 | |
| One course from Core Group B | 6 | |
| #colspan# | ||
| II semester | ||
| Probabilistic Machine Learning (++) | 6 | |
| Deep Learning (++) | 6 | |
| Advanced Statistical Methods | 6 | |
| One course from Core Group C | 6 | |
| One course from Core Group D | 6 | |
| #colspan# | ||
| II year (60 ECTS) | ||
| Three courses from Complementary Group | 18 | |
| Elective courses | 12 | |
| Intership | 12 | |
| Thesis | 18 | |
(+), (++): Integrated courses (two modules combined in a single course)
| Core Group A courses | ECTS |
|---|---|
| Advanced programming (*) | 6 |
| Machine Learning Operations (*) | 6 |
The course in Advanced Programming can be chosen only by students who does not have a solid background in programming (in C, C++ and Python), and who have not taken an advanced programming course during the bachelor. Please contact the program coordinator in case of doubts.
| Core Group B courses | ECTS |
|---|---|
| Introduction to Machine Learning (*) | 6 |
| Unsupervised Learning | 6 |
The course in Introduction to Machine Learning can be chosen only by students who have not taken an introductory course of machine learning during the bachelor or in other venues. Please contact the program coordinator in case of doubts.
| Core Group C courses | ECTS |
|---|---|
| Data Science for Insurance | 6 |
| Data Management (*) | 6 |
| Bayesian Statistics | 6 |
The course in Data Management can be chosen only by students who have not taken an course of databases or data management during the bachelor or in other venues. Please contact the program coordinator in case of doubts.
| Core Group D courses | ECTS |
|---|---|
| Ethics and Law of Data and Artificial Intelligence | 6 |
| Entrepreneurship and Business Modelling | 6 |
You can add complementary courses from the following group:
| Complementary Group | ECTS |
|---|---|
| Statistical Analysis of Social Networks | 6 |
| Data Science for Insurance | 6 |
| Natural Language Processing | 6 |
| Bayesian Statistics | 6 |
| Multi-Agent Systems | 6 |
| Unsupervised Learning | 6 |
| Information Retrieval and Data Visualisation | 6 |
You can add in the study plan elective courses from the following group:
| Elective courses | ECTS |
|---|---|
| All the courses in previous tables | |
| Business Analytics | 6 |
| Quantitative Finance | 6 |
| Computer Vision and Pattern Recognition | 6 |
| Advanced Topics in Machine Learning | 6 |
| Explainable and Reliable Artificial Intelligence | 6 |
| Mathematical Optimisation | 6 |
| Advanced Data Management | 6 |
| Software Development Methods | 6 |
| Machine Learning Operations | 6 |
| Time-series analysis | 6 |
| Symbolic and Neuro-Symbolic Artificial Intelligence | 6 |
| Stochastic Modelling and Simulation | 6 |
| Simulation Intelligence and Learning for Autonomous Systems | 6 |
| High Performance Computing and Data Infrastructures | 6 |
| Other courses (****) |
(***) Any other course from the University consistent with the study plan