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