BUSINESS INTELLIGENCE

Course Information

TitleΕΠΙΧΕΙΡΗΜΑΤΙΚΗ ΕΥΦΥΙΑ / BUSINESS INTELLIGENCE
CodeIM-120
Interdepartmental ProgrammeInterdisciplinary PPS on Informatics and Management (2014-today)
Collaborating SchoolsInformatics
Economics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorIoannis Vlachavas
CommonNo
StatusActive
Course ID40003136

Programme of Study: Interdisciplinary PPS on Informatics and Management (2014-today)

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
EconomicsCompulsory116

Programme of Study: Interdisciplinary PPS on Informatics and Management 2015

Registered students: 13
OrientationAttendance TypeSemesterYearECTS
OIKONOMIKOCompulsory117.5

Class Information

Academic Year2015 – 2016
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600011019
DeletedYes
Type of the Course
  • Scientific Area
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge of probability theory
Learning Outcomes
a. Describe course objectives / outcomes and competences (knowledge & skills): Cognitive: Understanding the basic theory for simple and sequential decisions by an intelligent agent, familiarization with the types of decision support systems, understanding the basic principles of game theory, understanding the theory of decision making with data analysis, understanding the developing decision support systems, familiarization with the use of decision support software. Skills: Training on developing decision support systems, training on the use of decision support tools.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Business Intelligence and Decission Support Systems, Decision Making under Certainty(Multi-criteria Decision Making Methods), Decision Making under Ignorance, Decision Making under Risk (Probability Theory, Bayesian Networks, Utility Theory), Sequential Decisions (Decision Trees, Decision Making in the presence of Competitive Agents (Game Theory), Decision Making with Data Analysis (Machine Learning, Knowledge Discovery in Databases, Business Intelligence Software Applications.
Keywords
Decision Making, Business Intelligence
Educational Material Types
  • Notes
  • Slide presentations
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures391.3
Reading Assigment240.8
Project421.4
Written assigments602
self-study602
Total2257.5
Student Assessment
Description
Final Exams (at the end of the semester): 80% and Presentation :20% . Bonus grades from projects
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Summative)
  • Written Exam with Short Answer Questions (Summative)
  • Written Exam with Extended Answer Questions (Summative)
  • Written Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Διδακτικές Σημειώσεις (διαφάνειες & πρόσθετο υλικό)
Additional bibliography for study
- Data science for Business, Foster Provost & Tom Fawcett, O'Reilly Media, 2013. - Making Hard Decisions: An Introduction to Decision Analysis, 2nd Edition, Robert T. Clemen, Duxbury Press, 1996. - Essentials of Management Information Systems, 4th Edition, J. Laudon, Prentice Hall, 2001. - Τεχνητή Νοημοσύνη, Γ' Έκδοση, Ι.Βλαχάβας, Π.Κεφαλάς, Ν. Βασιλειάδης, Φ.Κόκκορας και Η. Σακελλαρίου. Εκδόσεις Β.Γκιούρδας, 2006. - Decision support systems: concepts and resources for managers, Power, D. J., Westport, Conn., Quorum Books, 2002. - Data Science for Business, Foster Provost and Tom Fawcett, O'Reilly Media, 2013 - Practical Data Science With R, Nina Zumel and John Mount, Manning Publications, 2014
Last Update
20-04-2016