Business Intelligence and Applications
Persone
Docente titolare del corso
Descrizione
The first part of the course addresses core topics, such as enterprise data integration, visualization, and data mining. The second part gives an outlook on emerging data architectures, including social network structures and Big Data analytics.
Course outline:
- Data management architectures.
- OLAP and OLTP.
- Data warehouse architecture and design.
- Data Mining: clustering, classification, association rules.
- Visualization.
- Networks: models and centrality measures.
- A selection of topics among Big Data, NoSQL databases, ranking.
Obiettivi
The course develops a working knowledge of the principles, architectures, and tools for Business Intelligence.
Modalità di insegnamento
In presenza
Impostazione pedagogico-didattica
Besides traditional classroom-taught lectures, the course include discussions with students, live demos of tools, presentation of groupwork, and homework with individual assignments.
Modalità d’esame
The evaluation consists of:
- a written midterm test;
- a written final test;
- several small project assignments.
Bibliografia
Approfondimento
- Easley, David, Kleinberg, Jon. Networks, crowds, and markets: reasoning about a highly connected world. Cambridge :: Cambridge University Press, 2010.
- Franks, Bill. Taming the big data tidal wave: finding opportunities in huge data streams with advanced analytics. Hoboken: John Wiley, 2012.
- Han, Jiawei, Kamber, Micheline, Pei, Jian. Data mining: concepts and techniques. 3rd ed.. San Francisco, Calif. etc.]: Morgan Kaufmann, imprint of Elsevier, 2012.
- White, Tom. Hadoop: the definitive guide. 4th ed.. Beijing: O'Reilly, 2015.
Offerta formativa
- Master of Science in Artificial Intelligence, Lezione, A scelta, 1° anno
- Master of Science in Artificial Intelligence, Lezione, A scelta, 2° anno
- Master of Science in Management and Informatics, Lezione, 1° anno