During this course, the students are expected to learn the foundations of data mining and gain hands-on experience in applying data mining in practice. After taking the course, each student:
- Understands and can explain the basic principles and techniques of data mining.
- Is aware of various application areas of data mining.
- Understands and can explain when data mining might be useful.
- Can choose appropriate techniques for data preprocessing, basic modeling and evaluation, optimization of parameters for defined KPIs, e.g. cost-sensitive classification, for the algorithms available in Weka;
- Can make valid conclusions about the performance of the models and their utility for addressing the identified business problem.
- Is capable of translating business problems to data mining tasks and choosing appropriate data mining techniques.
- Has the skills for designing, developing and evaluating data mining solutions using existing data mining software.
The course fits with the educational philosophy of the program by emphasizing the interdisciplinary perspective of data science and introducing students to research in the field of data science.
Data mining studies how to induce predictive models and to gain useful actionable insights from the data with help of some computational tools. This introductory course covers the following topics:
- Data mining end-to-end process, starting with the translation of the business problem to data mining task(s) and data preparation for modeling and ending with evaluation of the data mining outcomes and reporting.
- Data mining techniques for classification, clustering, frequent itemset and association rule mining, feature subset selection and data transformation.
- Witten, I.H., Frank, E., & Hall, M.A., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011, ISBN 978-0-12-374856-0.