Course Structure

The program is designed around four pillars: Software Engineering, Statistical techniques, Telecommunication Engineering, and Business. All these subjects are needed to acquire the capacity to design, manage, and analyse Big Data.
The course implies activities for a total of 60 university credits (ECTS), 51 acquired through lectures and practical classes, and 9 acquired through a written final work (Master Thesis).
Class attendance will be compulsory. Classes will be held from Monday to Friday for 30 hours a week on average.
Lessons are organized in three terms. In the first two terms, which are the core of the Program, the student will receive the basic knowledge needed to manage and analyze Big Data. In the third term the student can choose 5 out of 10 elective courses.
Lessons will start on 17 January 2022, and will end in early June 2022.
Exams will take place in June-July 2022.
After taking the exams, students shall produce a written final work (Master thesis), which corresponds to 9 ECTS. The Master thesis will be discussed in December 2022. Its topic should be agreed with the coordinator of the program.
The Master thesis could be also carried out during an internship in Italian or European companies and institutions. During the visiting as an intern, the student will have two tutors, one selected by the Master Coordinator and the second one indicated by the hosting institution.

E-Learning – Master in Big Data A.Y. 2021/22

Given the medical emergency from Covid-19 still in progress, the Board of Directors of the Master in Big Data has decided that the ordinary teaching method for the next edition of the master will be online learning through the Microsoft Teams platform. Teachers will record all the classes also to allow attendance of working students. If possible, the exercises/lab lessons will be taught in a blended mode and be available in presence or in streaming.



I term: 18 ECTS
During the first term students will attend the following courses:

Courses Scientific Disciplinary Sector (SDS) Theoretical classes Practical classes ECTS credits
Supervised learning SECS-S/03 36 18 6
Unsupervised learning SECS-S/01 36 18 6
Data management for big data analysis INF/01 18 9 3
Security & Privacy ING-INF/03 18 9 3

During the second term students will attend the following courses:II term: 18 ECTS

Courses Scientific Disciplinary Sector (SDS) Theoretical classes Excercises and Seminars ECTS


High Dimensional Time Series SECS-S/03 18 9 3
Topics in machine learning INF/01 24 12 4
Architectures and systems for big data INF/01 18 9 3
Cloud & mobile ING-INF/03 12 6 2
Designing communication of results SECS-P/10 12 2
Decision making processes & models SECS-P/10 12 2
Strategic management of results SECS-P/10 12 2

III term: 15 ECTS

In the third term, the student should obtain 15 ECTS out of the following elective courses:

Courses Scientific Disciplinar Sector (SDS) Theoretical classes Excercises and seminars ECTS credits
Blockchain technology and applications ING-INF/03 18 9 3
Economic complexity FIS 02 18 9 3
Fundamentals of corporate finance SECS-P/10 18 9 3
Scientific data handling and image processing FIS/05 18 9 3
Network virtualization and softwarization ING-INF/03 18 9 3
Panel Data SECS-P/05 18 9 3
Social media analysis INF/01 18 9 3
Marketing Analytics Lab SECS-S/01 27
Text mining and document analysis INF/01 18 9 3
Business Practice of Data Science ING-IND/3 27