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
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.
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