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