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University of the Cumberlands Big Data Analytics Questions

University of the Cumberlands Big Data Analytics Questions

Question Description

I’m studying for my Computer Science class and don’t understand how to answer this. Can you help me study?

Chapter 8 –discussion question #1: How does prescriptive analytics relate to descriptive and predictive analytics ?

Chapter 8 –discussion question #2: Explain the differences between static and dynamic models. How can one evolve into the other ?

Chapter 8 –discussion question #3: What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty ?

Chapter 8 –discussion question #4: Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk.

Chapter 8 –discussion exercise #4: Investigate via a Web Search how models and their solutions are used by the U.S. Department of Homeland Security in the “war against terrorism”. Also investigate how other governments or government agencies are using models in their missions.

Chapter 9- discussion question #1: What is Big Data ? Why is it important ? Where does Big Data come from ?

Chapter 9- discussion question #2: What do you think the future of Big Data will be ? Will it lose its popularity to something else ? If so, what will it be ?

Chapter 9- discussion question #3: What is Big Data Analytics ? How does it differ from regular analytics ?

Chapter 9- discussion question #4: What are the critical success factors for Big Data Analytics ?

Chapter 9- discussion question #5: What are the big challenges that one should be mindful of when considering implementation of Big Data Analytics ?

Chapter 9- discussion exercise 18: Enter Humanyze.com. Review various case studies and summarize one interesting application of sensors in understanding social exchanges in organizations.

When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

All work must be original (not copied from any source).


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