Educational Structure
The Master has a year-long duration; it requires to acquire 60 CFU (university credits) accumulating 1500 hours of work split into frontal teaching, individual study and internship. Students are expected to take a final test consisting in the discussion of a Master Thesis.
The 1500 hours are divided as follows:
- 320 hours of frontal teaching and 730 hours of individual study corresponding to 42 CFU
- 400 hours of internship corresponding to 16 CFU
- 50 hours for the Final Test corresponding to 2 CFU
The teaching plan is structured into the following courses (click on the name of each course to see the detailed program):
Programming Methodology & Software for Business Intelligence
Data Management & Unstructured Data Processing
Big Data, Internet of Things, Data Gathering & Data Quality
Introductory Statistics
Data Mining I - Machine Learning, Text Mining & Large Language Models
Data Mining II – Statistical Information Synthesis
Parametric & Causal Models
Simulation Techniques
Statistical Interpretation & Communication
You can download the detailed program for each course. CLICK HERE
Attendance is mandatory.
Additionally, some hours of in-depth studying are being planned, with attendance being voluntary. Their emphasis will be on the application of specific software (such as SAS), laboratory practice, and real-world case scenarios. Generally, these lessons will be planned for Tuesday afternoons.
Click here to view the list of softwares used in each of the Master's courses.
Any updates will be published regularly on this section of the site.
In order to verify learning, ongoing checks will be carried out during the educational path with an evaluation expressed out of 30 with 18 as the minimum mark for passing. A final test is scheduled at the end of the whole Master, evaluated on a scale out of 110 with 66 as the minimum mark and consisting in the discussion of Master's thesis linked to the internship experience.
All tests must be passed successfully in order to obtain the Master's Diploma.