work

Please help with following written assignment:

Discussion and exercises:

Chapter 8:

  • Discussion 1: How does prescriptive analytics relate to descriptive and predictive analytics?
  • Discussion 2: Explain the differences between static and dynamic models. How can one evolve into the other?
  • Discussion 3: What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty?
  • Discussion 4: Explain why solving problems under uncertainty some- times involves assuming that the problem is to be solved under conditions of risk.
  • 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 1: What is Big Data? Why is it important? Where does Big Data come from?
  • Discussion 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?
  • Discussion 3: What is Big Data analytics? How does it differ from regular analytics?
  • Discussion 4: What are the critical success factors for Big Data analytics?
  • Discussion 5: What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

Your paper should meet these requirements:

  • Minimum 5 pages length (discussions ~2+exercise 1), not including the required cover page and reference page. Also do not include questions, just number and the answer.
  • Run your submission on a grammar check before submitting
  • Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
  • Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations.
  • References should be relevant and recent (in last 3 years). No bogus and made-up references
  • If you need to quote from reference, do not exactly quote, rephrase the quote as direct quote increases plagiarism percentage.
  • Be clearly and well-written, concise, and logical, using excellent grammar and style techniques.
  • No plagiarism
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