Data Mining - Classification
Part of the
Pathway in Introduction to Data Mining
About the CourseLearn how to formulate and solve classification problems for use in Data Mining and Business Intelligence applications such as; fraud detection, customer churning, network intrusion detection, etc... You will learn how to develop, validate and apply a data mining workflow to solve binary and non-binary classification problems. The course is self-contained, and it does not require any programming skills. Hands-on lectures are based on the KNIME open source software platform.
Learning outcomesBy the end of this course, you will be able to:
- develop a Data Mining workflow for solving a classification problem,
- apply elementary missing replacement strategies,
- apply pre-processing techniques including dimensionality reduction,
- select and deploy the “optimal classifier” (whatever it means) also taking into account decision costs,
- select relevant attributes and remove not relevant and/or redundant attributes.
You will learn all this using the KNIME open source platform, which integrates power and expressiveness of Weka, R and Java.
Background and Requirements
Basic knowledge of probability, statistics and mathematics.
- Pang-Ning Tan, Steinbach Michael and Vipin Kumar, (2006). Introduction to Data Mining. Morgan-Kaufmann.
Course FormatThe course spans four weeks. Each week requires 8 to 10 hours of work. Each week consists of 5 to 7 video-lectures. Each video-lecture consists of a methodology video, a software usage video and a practice session.
Certificates and Exam rules
You must accomplish all practice sessions associated with lectures, and then upload, to the course platform, the corresponding KNIME workflow you developed .