1.6.1: Review Questions
- Page ID
- 58281
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Review Questions
Multiple Choice
-
Which type of analytics answers the question "What should we do about it?"
- a) Descriptive
- b) Diagnostic
- c) Predictive
- d) Prescriptive
-
In the data-to-insight lifecycle, which stage typically consumes the most time?
- a) Data collection
- b) Data cleaning and preprocessing
- c) Data visualization
- d) Insight to action
-
Which role is primarily responsible for building and maintaining data pipelines and infrastructure?
- a) Data Analyst
- b) Data Scientist
- c) Data Engineer
- d) Business Analyst
Short Answer
-
Explain the difference between descriptive and diagnostic analytics. Provide one example of each using an e-commerce scenario.
-
List and briefly describe the seven stages of the data-to-insight lifecycle. Why is the lifecycle described as iterative rather than strictly linear?
-
Compare the roles of data analyst, data scientist, and data engineer. What skills are unique to each, and where do they overlap?
-
Using the MobileFlow case study from the chapter, explain how each of the four analytics types could be applied to the customer churn problem.
True/False
-
True or False: Predictive analytics guarantees what will happen in the future with 100% accuracy.
-
True or False: The data-to-insight lifecycle always proceeds in a strict linear sequence with no revisiting of earlier stages.
-
True or False: Data analytics overlaps with related fields such as business intelligence and data science, but each has a distinct scope.
Answer Key:
- d
- b
- c
- Descriptive: "Our sales dropped 20% last week" (summarizes what happened). Diagnostic: "The drop coincided with a website update that slowed page load by 40%" (explains why).
- Define goals → Collect data → Store/manage → Clean/preprocess → Analyze/model → Visualize/communicate → Act on insights. Iterative because findings may prompt new questions, reveal data gaps, or require revisiting earlier steps.
- Analyst: SQL, visualization, reporting, answers specific questions. Scientist: ML, advanced stats, programming, builds predictive models. Engineer: infrastructure, pipelines, databases, ensures data flows. Overlap: all use data, need domain knowledge, collaborate on projects.
- Descriptive: "Churn increased 15% in Q4." Diagnostic: "Users who engaged with social features <2x/week churned more." Predictive: "Model forecasts continued 15-20% decline without action." Prescriptive: "Launch targeted engagement campaign for at-risk users."
- False
- False
- True