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.
