Many decisions in professional and private life are taken on the basis of data that come from all sorts of information systems. Business Intelligence (BI) is 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. BI is an umbrella term that combines the processes, technologies, and tools needed to transform data into information, information into knowledge, and knowledge into plans that drive profitable business action. BI encompasses: data warehousing, OnLine Analytical Processing (OLAP), business analytical tools, data mining, business performance and knowledge management.
The commercial interest in BI is growing 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 BI/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: cross-selling, churn in mobile communications, and risk analysis in financial services. The emphasis in this course will be on the methodological and practical aspects of BI.
In this course the student is given an introduction in decision support Systems and machine learning within the framework of BI. After this course the student has basic knowledge of
why computer support is needed for certain business decisions
the principles of knowledge Management and knowledge-based Systems towards a smart enterprise
the business implications of a data warehouse
OLAP database technology, reporting and visualization
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 chain
foundations of data mining and machine learning models: o Decission Trees, Random Forest o Rule-based Systems o Neural Networks and Deep Learning
performance issues, interpretation, and the business relevance of data mining models.
The schedule can be found on the LIACS website
Detailed table of contents can be found in blackboard.
Mode of instruction
3 hours lectures every week.
Final grade (F) = 0.7∙E + 0.15∙A1 + 0.15∙A2, where E is the Exam grade, and A1 and A2 are grades for assignments.
Decision Support and Business Intelligence Systems (9th International Edition, 2010)
Notice that in the lectures only a limited number of chapters is discussed.
Will be made available via Blackboard.
Signing up for classes and exams
You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.
There is a limited capacity for students from outside the master ICT in Business. Please contact the Programme Co-ordinator.
Programme Co-ordinator ms. Judith Havelaar LL.M