Program Overview

Data Analytics Program

Overview

The Data Analytics course deals with mining very large datasets, analyzing them to make descriptive summaries of their content, test hypotheses, and extract valuable knowledge from them. This course covers topics such as similarity measures for very large datasets, mining fast data streams, link analysis, clustering, recommender systems, and more.

Objectives

The primary objective of this course is to teach students how to perform practical analysis of large datasets to interpret, visualize, and diagnose results and potential problems.

Teaching Methodology

The course consists of lectures where methods are analyzed from a theoretical perspective, and a practical part where theory is put into practice using statistical packages in Python. The aim is to teach, mostly by example, how to analyze large datasets.

Examination Method

Students are examined during the course through 2 theoretical and 1-2 practical tests. The theoretical tests cover material taught during theoretical lectures, while practical tests involve analyzing individually assigned large datasets.

Required Reading

• Mining of Massive Datasets by Leskovec, Jurij, Rajaraman, Anand, Ullman, Jeffrey D., Third edition, Cambridge University Press, 2020.

Program Details

The Data Analytics course is part of the following programs:

• Master of Science in Artificial Intelligence, Lecture, 1st year
• Master of Science in Computational Science, Lecture, Elective, 1st and 2nd year
• Master of Science in Informatics, Lecture, Information Systems, Elective, 1st and 2nd year

Course Information

• Semester: Spring
• Academic Year: Available
• ECTS: 6.0
• Language: English

Campus Information

The course is available at the following campuses:

• Campus Lugano
• Campus Mendrisio
• Campus Bellinzona