What is the value of Security Plans, Policies, Procedures, and Models? (What is the purpose or
values of each and how do they relate to each other?) (in terms of data context)
Share any experience involving “dirty data” from your personal or working environment. (If you cannot identify one, provide a hypothetical situation.) Give a reasonable explanation for it and how it could have been prevented. Discuss how data pre-processing would address this issue. Be sure to relate your discussion with the textbook readings in order to get full credit.
What is discretization?
Provide a one-paragraph discussion for each of equalinterval, equal frequency, k-means clustering discretization.
Compare and contrast the discretization methods above providing at least one example of when you would use each one.
Discussion of when and why variables should be removed from a dataset.
Why is it important to handle missing values in your dataset prior to beginning your primary data analysis? Provide a one-paragraph, masters-level response in your own words.
Describe at least one alternative approach for handling missing values other than replacing the values with the attribute mean. Provide a one-paragraph, masters-level response in your own words.
Discus where you provide at least two reasons why data should be sorted.
Discus in a one-paragraph, masters-level interpretation of what the plot function shows.
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