Problem 1: Why is it important to explore data before executing any DM algorithms? What can go wrong? What can be discovered in an exploratory analysis?
Problem 2: What are the three types of missing values? Give examples.
Problem 3: Suppose you are given two datasets: a survey result about satisfaction with a clinic visit and patient records. For privacy reasons personal information (name, record number, address) has been removed from both datasets. Your goal is to find out if there is any relationship between the survey results and severity of cases (severity can be obtained form medical records).
Problem 4: How do you approach the problem of linking the two datasets? Speculate on what attributes you would use to link them.
Problem 5: Should you assume that medical record is found for every patient who completed survey?
Problem 6: Should you assume that survey is found for every patient who was treated at the clinic?