go beyond the numbers: translate data into insights coursera weekly challenge 1 answers

Test your knowledge: Tell stories with data

1. Fill in the blank: The presenting stage of exploratory data analysis involves sharing _____, which can include graphs, charts, diagrams, or dashboards.

  • data frames
  • databases
  • data visualizations
  • datasets

2. During which exploratory data analysis practice might a data professional familiarize themself with the meaning of column headers in a dataset?

  • Structuring
  • Joining
  • Validating
  • Discovering

3. If sampled data is organized in such a way that it does not accurately represent its population as a whole, what problem will occur?

  • Unclean data
  • Disorganized data
  • Unfiltered data
  • Biased data

Test your knowledge: How PACE informs EDA and data visualizations

4. What are the primary drivers of a data-driven story? Select all that apply.

  • Stakeholder theories
  • Project purpose
  • Project goals
  • Sales predictions

5. Fill in the blank: In order to help avoid _____ in the workplace, data professionals share the PACE plan with stakeholders and team members.

  • competition
  • unnecessary meetings
  • miscommunication
  • unintentional bias

6. Why is it important to maintain proper scale of a graph’s axes in a data visualization?

  • To take advantage of white space
  • To tell a more interesting data story
  • To change stakeholders’ minds
  • To avoid misrepresenting the data

Weekly challenge 1

7. Fill in the blank: Exploratory data analysis is the process of investigating, organizing, and analyzing datasets and _____ their main characteristics.

  • summarizing
  • modifying
  • preparing
  • augmenting

8. A data professional is familiarizing themselves with a dataset to determine how many total data points it contains. Which exploratory data analysis process does this scenario describe?

  • Joining
  • Cleaning
  • Validating
  • Discovering

Shuffle Q/A 1

9. What are the goals of the structuring exploratory data analysis step? Select all that apply.

  • Group data in such a way that it accurately represents the dataset as a whole.
  • Prepare data to be effectively modeled.
  • Make data easier to visualize and explain.
  • Correcting misspellings or other errors.

10. Which of the following statements correctly compare data cleaning to data validation during exploratory data analysis? Select all that apply.

  • Cleaning is the process of confirming that no errors were introduced during validation.
  • Validating involves verifying the data is of high quality.
  • Cleaning involves ensuring the data is useful.
  • Both data cleaning and data validation involve eliminating any misspellings in the data.

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