Data Analytics
Full course description
This course treats the theory and practice of Business Analytics, data mining, process mining and simulation. Methods for the analysis of data are presented, from current data analytics toolboxes. We study how (and how not) to build predictive models to extract information from large databases and how to interpret the results. The thus discovered knowledge is used for intelligent decision making to make processes run more efficiently and to develop new services for the organizations that provide the data.
The course aims at getting hands-on experience in analyzing managerial decision processes, based on available data from real-life cases. The course consists of applying up-to-date data analytics techniques on real-life problems. These techniques will be implemented with modern software tools (Excel, Tableau, Celonis & Knime).
Course objectives
- This course aims at getting hands-on experience in analyzing managerial decision processes, based on available data, and using quantitative techniques for decision making.
Prerequisites
SCI2033 Data Mining.
Recommended
SSC2061 Statistics 1.
Recommended reading
- 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.
Recommended:
- Cole Nussbaumer Knaflic (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley. ISBN-10: 1119002257, ISBN-13: 978-1119002253