At the end of this course, students will be able to

**O1:** Know basic knowledge discovery concepts

**O2:** Interpret the characteristics of data set using statistical techniques

**O3:** Identify the appropriate knowledge discovery steps for a given problem

**O4:** Analyse the quality of a data set

**O5: **Know and apply basic pre-processing techniques in data mining

**O6**: Use data mining software for solving practical problems in different case studies such as customer segmentation, process control, etc.

**O7:** Understand basic classification and clustering techniques and judge the adequacy of each technique for a given data set

**O8:** Understand the purpose of error measures, bias and variance

**O9: **Know to measure model performance

**O10:** Use visualization techniques to illustrate various characteristics of a data set

**O11:** Understand basic association rule mining algorithms and apply them on a data set

**O12:** Gain experience of doing independent study and research on topic