Admission requirements
None
Description
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.
Course objectives
IIn this course the student is given an introduction intelligent systems 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;
why a data warehouse is needed;
the business implications of a data warehouse;
OLAP database technology and reporting;
the fundamental issues of knowledge discovery in databases, such as learning algorithms for classification, prediction and risk analysis;
a key data mining models: decision trees and/or neural networks;
In addition, the skill objective for the course is to give the student some hands on experience with BI software. By working with the software in the course and at home the student has to develop basic knowledge to analyze a simple data set with R.
Timetable
The schedule can be found on the Leiden University student website
Detailed table of contents can be found in blackboard.
Mode of instruction
4 days lecture.
Assessment method
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.
Blackboard
Reading list
Grossmann, W. and Rinderle-Ma, S., 2015. Fundamentals of Business intelligence. Springer.
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 only limited capacity for external students. Please contact the programme Co-ordinator
Contact information
Programme Co-ordinator: ms. Esme Caubo