1. What are the three characteristics of Big Data, and what are the main considerations in processing Big Data? 2. What is an analytic sandbox, and why is it important? 3. Explain the differences between BI and Data Science. 4. Describe the challenges of the current analytical architecture for data scientists. 5. What are the key skill sets and behavioral characteristics of a data scientist? 6. In which phase would the team expect to invest most of the project time? Why? Where would the team expect to spend the least time? 7. What are the benefits of doing a pilot program before a full-scale rollout of a new analytical method- ology? Discuss this in the context of the mini case study. 8. What kinds of tools would be used in the following phases, and for which kinds of use scenarios? a. Phase 2: Data preparation b. Phase 4: Model building

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