This is a specialisation course. It depends upon the number of participants whether the course will take place
Many decisions in professional and private life are taken on the basis of data that come from all sorts of business information systems. Business Intelligence (BI) and Business Analytics (BA) are about the developments in the way we can use data stored in those information systems, to generate new and useful information that can support executive managers in taking business decisions. Business Analytics aims at developing new insights and understanding of business performance based on data and computerized methods. In the so-called Big Data era it is considered an essential skill for any digital manager. BA encompasses, amongst others: data warehousing, OnLine Analytical Processing (OLAP), business intelligence tools, data mining, business performance and knowledge management.
The commercial interest in BA is growing rapidly due to the increasing awareness of companies that the vast amounts of data collected on customers and their behaviour contain valuable business knowledge. Different types of knowledge can be derived from data warehouses, like rules characterizing potential customer classes, knowledge classifying groups with larger risks, and so on. Quite often useful causal relations are hidden in company databases and the goal of the BA/data mining process is to induce these from the data and to represent them in meaningful ways to improve business processes. Typical business cases are: demand forecasting, customer segmentation, risk analysis in financial services, fraud detection, and performance management. The emphasis in this course will be on the methodological and practical aspects of BA.
In this course the student is given an introduction in BI and BA. After this course the student has basic knowledge of:
• why computer support is needed for certain business decisions;
• the principles of knowledge management en knowledge-based systems;
• the construction of a data warehouse;
• the business implications of a data warehouse;
• OLAP database technology and reporting;
• the fundamental issues of knowledge discovery in databases, i.e. data mining, such as learning algorithms for classification, prediction and risk analysis;
• the data mining process;
• key data mining model: decision trees and neural networks;
• performance issues, interpretation, and the business relevance of data mining models.
In addition, the skill objective for the course is to give the student some hands on experience with business analytics software. By working with the software (at home) the student
• has to develop a basic knowledge-based system (software Exsys Corvid);
• has to analyze a business data set with a data mining tool (software WEKA or R).
The schedule can be found on rooster
Mode of instruction
The course combines lectures, case studies, interactive discussions, assignments, research, a final paper and a final exam. Students are required to fill in expected study efforts (SBUs) by co-operating, self-study and to explore literature on available resources such as libraries, internet, etc. There is a preparatory assignment before the first meeting.
Final grade (F) = 0.5∙P + 0.1∙A1 + 0.1∙A2+0.3.E, where E is the Exam grade, P is the paper and A1 and A2 are grades for assignments.
• Book (Recommended). Business Intelligence and Analytics: Systems for Decision Support, 10/E, Sharda, Delen, and Turban, ISBN-10: 1292009209, ISBN-13: 9781292009209
• Scientific articles. All required literature will be made available to students on the Blackboard in electronic form.
For more information, please contact Programme Co-ordinator ms. Judith Havelaar LL.M