Business Intelligence and Data Mining Final Exam
1) BrieflyDifferentiate between Business Intelligence and Data Mining
2) What are Association Rules and Provide an example
3) Explain Data Visualization. Provide a modern example
4) What are the two main types of variables? Provide an example of each
5) When thinking about Outliers, what is a simple way to find variables that are outliers, in Excel for instance. Once you identify an outlier, what sort of expertise would be called upon to determine if the outlier truly is?
6) Network Graph Y or N? Why?
7) Heatmap Y or N? What does this output tell about where to place products if this is a representation of where people move and congregate within this store?
8) For this Neural Network, label input, hidden layer and output. How many hidden layers?
9) How do neural networks adjust and learn to take inputs and ensure the outputs match as close as possible to the intended data? What is the name of the term? Provide an example.
10) For TimeSeries Forecasts so reliant on large, immediate data sources, what is the inherent problem with capturing real-time data?
11) Comment on Time Series Data assisting a restaurant on Cape Cod or up on Cape Ann. What will datasets of customer volume help predict? Is there a financial savings to be gained by applying this data? Hint: labor, food, utilities costs....
12) Label only 2 nodes and 2 edges in the diagram below. Who is the most connected member?
13) Are the two networks below equal ? Why or Why Not?
14) Applying the concept of Tokenization, how many tokens in the phrase; "132 Eastham Lane, Chatham, MA 02633". Do not count the parenthesis.
15) What is a stoplist?