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Business Intelligence and Applications


The course develops a working knowledge of the principles, architectures, and tools for Business Intelligence. 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. The evaluation consists of: - a written midterm test; - a written final test; - several small project assignments; - a short oral discussion.




  • Main references: Class handouts.

Suggested reading:

  • Data Mining: Concepts and Techniques; Jiawei Han, Micheline Kamber, and Jian Pei, 3rd edition, Morgan Kaufmann, 2011
  • Networks, Crowds, and Markets: Reasoning about a Highly Connected World, David Easley and Jon Kleinberg, Cambridge University Press, 2010
  • Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale, Tom White, 4th Edition, O'Reilly
  • Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics. Bill Franks, John Wiley & Sons, 2012.
  • Further reading, in the form of books and/or scientific articles, will be suggested in class.



Martinenghi D.

Course director

Humbatova N.


Additional information

Academic year
Master of Science in Artificial Intelligence, Elective course, Lecture, 2nd year
Master of Science in Artificial Intelligence, Elective course, Lecture, 1st year
Master of Science in Financial Technology and Computing, Elective course, Lecture, 2nd year
Master of Science in Management and Informatics, Core course, Lecture, 1st year