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