11. Fill in the blank: A data professional discovers that their dataset does not have enough data. Therefore, they choose to add more data during the _____ process.

  • joining
  • validating
  • structuring
  • cleaning

12. What may be involved with visualizing data during exploratory data analysis? Select all that apply.

  • Considering people with visual impairments by describing the data in detail
  • Asking stakeholders to hold their comments until the final official presentation
  • Considering people with auditory impairments by providing captioned descriptions about the data
  • Making data visualizations available to team members for further analysis or modeling

13. What are some strategies that a data professional might use to help avoid miscommunication in the workplace? Select all that apply.

  • Share the PACE plan with all stakeholders.
  • Provide audiences with raw data for their own exploration.
  • Understand stakeholders’ most important goals before presenting to them.
  • Present primary analysis with a working group to get feedback.

14. Fill in the blank: The exploratory data analysis process is_____, which means data professionals often work through the six practices multiple times.

  • transitory
  • supplementary
  • iterative
  • immutable

15. What processes do data professionals perform during the structuring exploratory data analysis step? Select all that apply.

  • Organize raw data.
  • Categorize data into categories representing the dataset.
  • Create data visualizations.
  • Transform raw data.

16. What steps may be involved with presenting data insights to others during exploratory data analysis? Select all that apply.

  • Make the visualizations available to others for further modeling
  • Share a cleaned dataset for additional analysis
  • Remove written descriptions to save people time when viewing the visualizations
  • Ask team members or stakeholders for feedback

17. Fill in the blank: To avoid miscommunication in the workplace, data professionals can share _____ with a working group to get early feedback.

  • metadata
  • initial data findings
  • raw data
  • changelogs

18. Fill in the blank: The type of data being studied and the _____ guide the order of the six practices of exploratory data analysis.

  • size of the dataset
  • available hardware and software
  • needs of the data team
  • company mission

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

  • Validating is the process of removing any errors in the data. Cleaning is the process of confirming that the data-validation process did not introduce any errors.
  • Data cleaning involves eliminating any misspellings in the data. Validating does not.
  • Cleaning involves ensuring the data is useful. Validating involves verifying the data is of high quality.
  • When cleaning, a data professional looks for missing values, duplicate entries, and extreme outliers. When validating, a data professional uses digital tools to confirm the data types within a dataset.

20. A data professional is beginning to conceptualize a dataset and investigating the meaning of its column headers. Which exploratory data analysis process does this scenario describe?

  • Discovering
  • Joining
  • Cleaning
  • Validating

21. Fill in the blank: In exploratory data analysis, _____ is the process of augmenting a dataset by adding values from other sources.

  • cleaning
  • validating
  • joining
  • structuring

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