Course Structure

The Master degree in Data Science and Economics is a genuinely multidisciplinary programme, offering a well-balanced set of courses in data science and economics supported by several other courses. Students must acquire 120 ECTS to complete the programme; among them, 24 credits are devoted to additional educational and research activities, for example dissertation writing, research seminars and elective courses.

The following are mandatory for all students:

First year

COURSES ECTS   SSD TERM
Advanced Microeconomics and Macroeconomics 12 SECS-P/01 2 – 3
Coding for Data Science and Data Management 12 INF-01, SECS-S/01 2 – 3
Graph Theory, Discrete Mathematics and Optimization 12 SECS-S/06, MAT-09 1
Machine Learning, Statistical Learning, Deep Learning and Artificial intelligence 12 INF-01, SECS-S/01 2 – 3
Micro-eonometrics, Causal Inference and Time Series Econometrics 12 SECS-S/01, SECS-P/05 1 – 2
Total number of credits earned at the end of the first year   60    

Second year

 COURSES ECTS   SSD TERM
Algorithms for Massive Data, Cloud and Distributed Computing 12 INF-01 2 – 3
Cybersecurity and Privacy Preservation Techniques and Digital Security and Privacy 6 INF/01, IUS/09, IUS/14 1
Cumulative number of credits earned after the second year’s mandatory courses   78    

Choose your specialization path

At the end of the first year, students have to choose among one of the three curricula called Economics, Business Innovation and Social Science.

Curriculum: Economics

(Total 18 credits/ects)

3 activities among the following, but not more than 2 among those marked by*: ECTS SSD TERM
Advanced Multivariate Statistics 6 SECS-S/01 1
Bayesian Analysis 6 SECS-S/01  
Experimental Methods And Behavioural Economics 6 SECS-P/02  
Fintech Industry * 6 SECS-P/11 2
Game Theory 6 SECS-P/01 1
Global Firms and Market 6 SECS-P/01 1
Knowledge Extraction and Information Retrieval 6 INF/01 2 – 3
Labour Economics And Policy Evaluation 6 SECS-P/03  
Numerical Methods for Finance 6 SECS-S/01 2
Patients’ Needs and Healthcare Markets 6 SECS-P/01 3
Portfolio Optimization* 6 SECS-S/06 1
Probabilistic Modeling  6 SECS-S/01 2
Quantum Finance* 6 SECS-S/06  
Risk Management* 6 SECS-S/06 2
Sampling Techniques For Big Data 6 SECS-S/01  
Scientific Data Visualization 6 INF/01  

Curriculum: Business Innovation

(Total 18 credits/ects)

3 activities among the following: ECTS SSD TERM
Advanced Multivariate Statistics 6 SECS-S/01 1
Bayesian Analysis 6 SECS-S/01
Digital Business Strategies 6 SECS-P/07 2
Fintech Industry* 6 SECS-P/11 2
Human Resource Management via Workforce Analytics 6 SECS-P/10  
Intellectual Property for Business: Strategy and Analysis 6 SECS-P/10 2
Knowledge Extraction and Information Retrieval 6 INF/01 2 – 3
Marketing Analytics 6 SECS-P/08 1
Open Data for New Business 6 SECS-P/08  
Probabilistic Modeling 6 SECS-S/01 2
Project Managements and Innovation in the Era of Big Data 6 SECS-P/08 3
Sampling Techniques for Big Data 6 SECS-S/01  
Scientific Data Visualization 6 INF/01  
Social Network Analysis 6 INF/01 1 – 2
Social Network Analysis for Business and Organization 6 SECS-P/08  
Text Mining and Sentiment Analysis 6 INF/01, SECS-S/01 2

Curriculum: Social Science

(Total 18 credits/ects)

3 activities among the following: ECTS SDD TERM
Advanced Multivariate Statistics 6 SECS-S/01 1
Bayesian Analysis 6 SECS-S/01
Communication Research 6 SPS/07  
Digital Society 6 SPS/07 1
Game Theory 6 SECS-P/01 1
Global Firms and Market 6 SECS-P/01 1
Labour Economics and Policy Evaluation 6 SECS-P/03  
Knowledge Extraction and Information Retrieval 6 INF/01 2 – 3
Patients’ Needs and Healthcare Markets 6 SECS-P/01 3
Probabilistic Modeling 6 SECS-S/01 2
Public Opinion Research 6 SPS/11 2
Sampling Techniques for Big Data 6 SECS-S/01  
Scientific Data Visualization 6 INF/01  
Social Network Analysis   6 INF/01 1 – 2
Social Network Analysis for Business and Organization 6 SECS-P/08
Text Mining and Sentiment Analysis 6 INF/01, SECS-S/01 2

N.B:

-Yellow course are provided by DSE
-Green courses are borrowed from MEF
-Blue courses are borrowed from EPS
-Red courses are borrowed from MIE
-Pink courses are borrowed from Informatics Department
-Light green courses are borrowed from COM
-White courses are not activated in 2019/2020

P.S: For mutual courses, there is no guaranteed time slot. Please check the sites of the individual degree courses.


Other activities for the completion of study program

To complete the study program the students have to choose additional elective course(s) or Laboratories for a total amount of 12 ECTS.

Master thesis & Stage

For the preparation of the final Master thesis, the students will earn 9 ECTS .
Moreover, DSE students will have to complete a  mandatory stage/internship equivalent to 3 ECTS.
The Master thesis might (and strongly suggested to) be related to the stage project.

The total number of credits at the end of the study program is 120 ECTS.

COURSE PROGRESSION REQUIREMENTS: none

Where to find details about each individual course?

You need to navigate the school website starting from this page.
The syllabus of each course can be found by clicking on “structure”.
In the “structure” page you can also find general information about the exams and the erogation period of the course.