Note: All questions carry equal marks
Q1. Give two examples, apart from those given in the slides, for each of the following:
a) Data mining from the commercial viewpoint
b) Data mining from the scientific viewpoint
Q2. Differentiate between classification of data and clustering of data with the help of suitable examples.
Q3. Why do we need preprocessing of the data? Explain any 4 data preprocessing techniques.
Q4. Explain in detail the 5 number summary of distribution (i.e. Minimum, Q1, Median, Q3, Maximum) of a box plot.
Q5. Give any two situations in which a distribution of data is negatively skewed in one and positively skewed in the other. You can think of any real life example.
quality work for all
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