Business Intelligence and Applications
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.
- Data management architectures.
- OLAP and OLTP.
- Data warehouse architecture and design.
- Data Mining: clustering, classification, association rules.
- Networks: models and centrality measures.
- A selection of topics among Big Data, NoSQL databases, ranking.
The course develops a working knowledge of the principles, architectures, and tools for Business Intelligence.
Besides traditional classroom-taught lectures, the course include discussions with students, live demos of tools, presentation of groupwork, and homework with individual assignments.
The evaluation consists of:
- a written midterm test;
- a written final test;
- several small project assignments.
- 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.
- Master of Science in Artificial Intelligence, Lecture, Elective, 1st year
- Master of Science in Management and Informatics, Lecture, 1st year