Data Mining and Business Intelligence

Code: MSC-4

Description: The concept of Decision Support Systems (DSS) and their role in decision making in business. Data mining and business intelligence. Supervised / unsupervised / semi supervised learning. Data preparation. Classification. Clustering. Association rules and sequential patterns. Data warehouses, OLAP. Multidimensional data management. Hypercubes and Hierarchies, Relational and multidimensional models, Tools for data mining and data warehousing - a comparative presentation. Knowledge extraction from texts. Data mining on the web. Building business intelligence from market data. Mining medical and biological data.

Objectives: The term Data Mining refers to the procedure of selection, investigation and analysis of large volumes of data in order to discover interesting patterns and rules. This procedure converts data into useful knowledge, which is essential for decision making in almost every organization. The course offers students the knowledge and skills needed to transform data into business intelligence. This is done gradually: first with the presentation of the basic techniques encountered in the literature, such as classification, clustering, finding association rules, etc., then with the practical application of the most popular algorithms and techniques in data sets with the assistance of open source tools and commercial software and end with case studies that demonstrate the increased interest in data mining and business intelligence both for research and business.

Teaching Methods: a) lectures, b) case studies, c) laboratory exercises, d) group assignments, e) students’ presentations, f) Invited lectures

Recommended Reading:

  1. Carlo Vercellis. “Business Intelligence: Data Mining and Optimization for Decision Making” Wiley. 2009
  2. Charu Aggarwal, ChengXiang Zhai. “Mining Text Data”, Springer 2012.
  3. Bing Liu. “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)”, Springer 2008.
  4. Ian Witten, Eibe Frank. “Data Mining, Practical machine learning tools and techniques” Elsevier, Morgan Kaufmann, 2005
  5. Rob Sullivan. “Introduction to Data Mining for the Life Science”. Springer 2012.
  6. Robert Stackowiak, Joseph Rayman, Rick Greenwald. “Oracle Data Warehousing and Business Intelligence Solutions”, Wiley, 2007
  7. Jiawei Han, Micheline Kamber. “Data Mining: Concepts and Techniques”, Elsevier, Morgan Kaufmann, 2006

    Prerequisites: -

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    9 υποτροφίες

    Το πρόγραμμα προσφέρει συνολικά εννέα (9) υποτροφίες στις καλύτερες επιδόσεις κάθε κατεύθυνσης σε κάθε εξάμηνο, οι οποίες καλύπτουν το σύνολο ή μέρος των διδάκτρων.

    Πλήρους Μερικής Φοίτησης

    Το Π.Μ.Σ. προσφέρει τη δυνατότητα πλήρους φοίτησης και μερικής φοίτησης. Η χρονική διάρκεια για την απονομή του Μεταπτυχιακού Διπλώματος Ειδίκευσης ορίζεται σε τρία (3) ακαδημαϊκά εξάμηνα για το πλήρους φοίτησης, ενώ για το μερικής φοίτησης η χρονική διάρκεια διπλασιάζεται.

    Μεταπτυχιακό Πρόγραμμα Σπουδών

    Στο Π.Μ.Σ. γίνονται δεκτοί ως υποψήφιοι πτυχιούχοι Τμημάτων Πανεπιστημίων της ημεδαπής ή αναγνωρισμένων ομοταγών ιδρυμάτων της αλλοδαπής, καθώς και πτυχιούχοι Τμημάτων ΤΕΙ συναφούς γνωστικού αντικειμένου.

    Department of Informatics & Telematics - 2017