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8.19S - Business Intelligence Systems

8.19S - Business Intelligence Systems

Business Intelligence Systems

Code: 8.19S

Semester: 8 / Year: 4 (Optional)

Teachers: Beligiannnis Grigorios

Course Web Page: https://eclass.upatras.gr/courses/DEAPT198/

Lectures hours (per week): 3

laboratory hours (per week):

Subject

  1. Computational intelligence algorithms and their applications in operations research problems and decision-making
  2. Analysis, design and implementation of business intelligence systems
  3. Design of business intelligence systems and application in the real world problems
  4. Probabilistic reasoning in business intelligence problems
  5. Learning from observations in business intelligence problems
  6. Adaptive business intelligence  
  7. Hybrid systems and adaptability 
  8. Distributed business intelligence systems

Educational Aims

The course offers the opportunity for students that will choose to attend it to get trained in specialty topics about implementing business intelligence systems and applying them to problems of management science.

The purpose of this course is to present and explain the specialized way in which someone can apply modern business intelligence systems to solve specific difficult problems of management science. With the help of concrete examples and exercises students can understand in depth how to resolve various complex and specialized management science problems, while trained to be able to apply such techniques and tools in order to solve specialized real world problems.

This course enables students to gain specialized knowledge required to be able to analyze, design and implement modern business intelligence systems to solve difficult modern problems of Management Science.

By the end of this course the student will be able to:

  • understand thoroughly the principles, operation and how to apply computational intelligence algorithms systems for solving Management Science problems
  • apply these systems to real problems from the field of economic and agronomic sciences, but also in their daily lives
  • use knowledge and understanding acquired in a manner that indicates a professional approach to their  work or profession
  • have competences typically demonstrated by developing and supporting arguments and solving problems within their field of knowledge
  • communicate information, ideas, problems and solutions to both specialist and non-specialist public
  • develop knowledge acquisition skills needed to continue to post graduate studies with a high degree of autonomy
  • gather and interpret relevant data (in their knowledge field) to form judgments that include reflection on relevant scientific issue
  • be able to use their knowledge, understanding and ability to solve problems in new or unfamiliar environment within broader (or multidisciplinary) context, related to their field
  • be able to communicate with clarity their conclusions, knowledge and reasoning in both specialized and non-specialized audience

Student Evaluation

Written examination after the end of the semester (100%) including:

  • Multiple-choice questions
  • Solving problems of designing and applying computational intelligence algorithm systems to management science problems
  • Benchmarking theory elements

Bibliography

  1. Θέματα Επιχειρηματικής Νοημοσύνης, Β. Βουτσινάς, 1η Έκδοση, Εκδόσεις Κωσταράκη, 2003 (in Greek).
  2. Εισαγωγή στην εξόρυξη δεδομένων, Tan Pang - Ning, Steinbach Michael, Kumar Vipin, 1η έκδοση, Εκδόσεις Α. Τζιόλα & Υιοί Α.Ε., 2010 (in Greek).
  3. Adaptive Business Intelligence, Z. Michalewicz, M. Schmidt, M. Michalewicz, C. Chiriac, 1st Edition, Publisher: Springer-Verlag, Berlin Heidelberg, 2006.
  4. Computational Intelligence: Principles, Techniques and Applications (Hardcover), Amit Konar, Hardcover:708 pages, Publisher: Springer; 1st edition (May 31, 2005), Language: English, ISBN: 3540208984.
  5. Computational Intelligence in Economics and Finance (Advanced Information Processing), S. H. Chen, P. Wang, Paul P. Wang Hardcover: 508 pages, Publisher: Springer; 1st edition (April 11, 2006), Language: English, ISBN: 3540440984.
  6. Introduction to Statistical Decision Theory, John W. Pratt, Howard Raiffa, Robert Schlaifer Hardcover: 904 pages, Publisher: The MIT Press (2 May 1995), Language English, ISBN: 0262161443.