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.
CORE SUBJECTS:
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
credits |
| 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 ECTSELECTIVE SUBJECTS:
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 |