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  • Part 1: 200-250 words with references
  • Provide an example of a data warehouse model defining the grain, dimensions and facts of the data warehouse.
  • Part 2: 200-250 words with references
  • Identify the importance of selecting the Grain of a data warehouse in the Kimball Data Warehouse Model.  Provide examples of grains within a Data Warehouse.
  • Part 3: 200-250 words with references (2 paragraphs)
  • Describe one unique and specific example where you would use classification of type Decision Tree, Bayesian or Rule-Based and explain WHY. Use references and justification to support your point of view.
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