foundations of data science coursera week 4 quiz answers

Test your knowledge: The data project workflow

1. In which PACE stage would a data professional ask, “What are the goals of the project?”

  • Plan
  • Construct
  • Execute
  • Analyze

2. Fill in the blank: In the _____ stage of the PACE model, a methodical examination of the data is conducted.

  • analyze
  • construct
  • execute
  • plan

3. In the execute stage of the PACE model, what is shared with stakeholders?

  • Datasets
  • Models and algorithms
  • Project goals
  • The story told by the data

Activity: Communicate with stakeholders in different roles

4. Did you complete this activity?

  • Yes
  • No

5. Which information is most relevant to include in the email to the new data professional on the team? Select all that apply.

  • The accuracy goal for the wildfire model
  • A description of the data team’s workflow
  • An invitation to ask follow-up questions
  • An overview of how predictive machine learning models work

6. Which information is most relevant to include in the email to the new writer for the agency’s public relations department? Select all that apply.

  • An overview of the National Parks Service’s wildfire project
  • Details about how much the project has improved the agency’s ability to predict wildfires
  • An invitation to ask follow-up questions
  • Details about the technical aspects of the project

Test your knowledge: Elements of communication

7. Which element of communicative exchange involves thinking about the reason why the communication is taking place?

  • Purpose
  • Sender
  • Receiver
  • Collaboration

8. To work successfully, PACE must be employed in a linear progression.

  • True
  • False

Shuffle Q/A 1

9. Fill in the blank: Regardless of the PACE workflow stage, _____ drives the framework through to project realization.

  • communication
  • observation
  • presentation
  • validation

Activity: Analyze a project proposal

10. What is the main goal of the project?

  • Scrub, convert, and format data
  • Develop a wildfire prediction engine to improve the prediction methods for the National Parks Service
  • Deliver a machine learning model
  • Test and retest machine learning techniques for accuracy

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