Full course description
This course treats the theory and practice of Business Analytics. Tools for the analysis of data are discussed, as well as methods for discovering knowledge from information and using this knowledge for intelligent decision making.
The course consists of applying up-to-date data mining techniques on real-life problems. These techniques will be implemented with modern software tools (SAS, SPSS modeller, Tableau, WEKA, XLMiner). We study how (and how not) to extract information from large data bases with standard techniques from data mining and how to interpret the results.
The first cases, selected from the literature, are used to get experience with the mentioned goals. The last two or three cases are selected from business practices based on current topical developments of the various disciplines involved with data oriented decision making: financial, marketing, supply chain management etcetera. These cases will be introduced by the selected companies. Some companies involved in previous years are: VISA (London), Proctor & Gamble (Brussels), and Smurfit-Kappa (Roermond).
- This course aims at getting hands-on experience in analyzing managerial decision processes, based on available data, and using quantitative techniques for decision making.
SCI2033 Data Mining.
SSC2061 Statistics 1.
- Data Science for Business, What You Need to Know about Data Mining and Data-Analytic Thinking, by Foster Provost and Tom Fawcett, O’Reilly Media 2013. ISBN 978-1-4493-6132-7, EBook ISBN 978-1-4493-6131-0 (not compulsory).
- Other materials, i.e. slides, selected scientific papers and data, will be made available through Student Portal.
- Cole Nussbaumer Knaflic (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley. ISBN-10: 1119002257, ISBN-13: 978-1119002253