After completing this course, students will be able to:
- apply the concepts of database management and use the Structured Query Language (SQL) to query and analyze business databases;
- comment on the development and business implications of data transformation and data warehouses;
- design and analyze an OLAP data cube for descriptive business analytics;
- comment on the fundamental issues of knowledge discovery in databases, i.e. (the) data mining (process), such as learning algorithms for classification and prediction. Discuss the business relevance of data mining models;
- interpret the substance of regression, fitting and supervised learning, including: interpreting parameters, designing the model, making choices;
- comment on the Nearest Neighbor algorithm. Perform classification on data tables with Naïve Bayes;
- design a decision tree on data;
- analyze data sets using frequent item sets/association rules;
- design clusters on data sets with different clustering techniques;
- evaluate performance issues, complexity issues, business relevance and implementation;
- work with (basic) data mining tools.
In computer labs you will work on practical exercises illustrating the theory. The exercises serve as a preparation for the exam.
The main issues in this course concern the identification and extraction of new and useful knowledge from company databases. We will start with the fundamentals behind these databases, and introduce you to database management and database querying with SQL. The company databases are the sources for the development of the data-warehouse, which is a dedicated database for managerial decision-making. In addition, different types of knowledge can be derived from data-warehouses. OLAP data cubes for descriptive business analytics, rules characterizing potential customer classes, knowledge classifying groups with larger risks, and so on. Quite often causal relations are hidden in company databases and the goal of the data mining process is to induce these from the data and to represent them in meaningful ways to improve business processes. The emphasis will be on the methodological and practical aspects of data mining.
Students may only participate in the examination after successful completion of several (lab) assignments (passed/not passed).
Type of instructions
Lectures, lab sessions, and self-study.
Type of exams
Written exam (100%) + See specifics.
- Shmueli, Galit, Patel, Nitin R, and Peter C. Bruce, Data-Mining for Business Analytics, Wiley, 2016, ISBN 9781118729274.
- Scientific articles, made available to students on Blackboard in electronic form.
- Turban et al., Business Intelligence, A Managerial Approach, Pearson, 2011, ISBN 0-13-247882-X.
- Han, J. & M. Kamber, Data Mining, Morgan Kaufmann Publishers, 2011, ISBN 1-55860-901-6.