Sample Questions for Test 1

 

 

1. Describe the characteristics of a Big Data set and what distinguishes it from an ordinary data set. How it is typically handled to derive benefits for the user? Give an example.
2. What are the benefits of Big Data processing? Give an example in use that shows how Big Data is used in a business setting.
3. Describe three Big Data tools and techniques and how they are used.
4. Data mining is an important concept that is used heavily in Big Data. Explain how data mining is used with Big Data. How do you prepare data for use in Big Data context?
5. How is predictive modeling used in Big Data? Explain. Give an example to illustrate your points.
6. Explain Big Data applications of Churn prediction and Fraud detection.
7. Explain Data Driven Decision-making. Give an example.
8. Describe the different types of probabilities that are needed in Big Data processing.
9. Describe the Big Data concepts of similarity, neighborhood and clustering.
10. How ethical issues arise in the handling of Big Data. Give examples of Big Data ethical issues.
11. Data reduction, causal modeling and co-occurrence are important Big Data concepts. Explain how these are used in Big Data. Give examples.
12. What is Data Science and what are the basic qualities that a data scientist must possess? Describe what roles some well-known Data Scientists play in US.

 

JHJ School of BusinessMIS 683-01Topics in MISSample Questions for Test 11.Describe the characteristics of a Big Data set and what distinguishes it from an ordinarydata set. How it is typically handled to derive benefits for the user? Give an example.2.What are the benefits of Big Data processing? Give an example in use that shows howBig Data is used in a business setting.3.Describe three Big Data tools and techniques and how they are used.4.Data mining is an important concept that is used heavily in Big Data. Explain how datamining is used with Big Data. How do you prepare data for use in Big Data context?5.How is predictive modeling used in Big Data? Explain. Give an example to illustrateyour points.6.Explain Big Data applications of Churn prediction and Fraud detection.7.Explain Data Driven Decision-making. Give an example.8.Describe the different types of probabilities that are needed in Big Data processing.9.Describe the Big Data concepts of similarity, neighborhood and clustering.10. How ethical issues arise in the handling of Big Data. Give examples of Big Data ethicalissues.11. Data reduction, causal modeling and co-occurrence are important Big Data concepts.Explain how these are used in Big Data. Give examples.12. What is Data Science and what are the basic qualities that a data scientist must possess?Describe what roles some well-known Data Scientists play in US.

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