11. A data analyst needs to quickly create a series of scatterplots to visualize a very large dataset. What should they use for the analysis?
- A dashboard
- The R programming language
- A slide presentation
- Structured query language
12. What type of software is RStudio?
- Integrated development environment
- Programming language
- Syntax
- Pane
13. A data analyst wants to write R code where they can access it again after they close their current session in RStudio. Where should they write their code?
- R console
- Files tab
- History tab
- Source editor
14. What are the benefits of using a programming language for data analysis? Select all that apply.
- They store steps of your analysis for future use.
- They have no specific syntax.
- They save time cleaning data.
- It does not require data cleaning
15. Which of the following statements about the R programming language are correct? Select all that apply.
- It can create world-class visualizations
- It makes analysts spend more time cleaning data and less time analyzing
- It can process large amounts of data
- It relies on spreadsheet interfaces to clean and manipulate data
16. A data analyst is searching for a tool that gives them the most power to customize the visualizations they use in their analysis. What tool should they use?
- The R Programming language
- Tableau
- Spreadsheets
- SQL
17. Which of the following statements about RStudio’s integrated development environment are correct? Select all that apply.
- R studio is unable to produce visualizations.
- R studio is built specifically for working with R.
- The layout of panes in R studio is fixed.
- R studio helps with file management.
18. R users share custom solutions they have developed for data problems. Where can you find this information in RStudio?
- Packages tab
- History tab
- Environment tab
- R console
Shuffle Q/A 2
19. What tool gives data analysts the highest level of control over their data analysis?
- Spreadsheet
- SQL
- Tableau
- Programming language
20. Using a programming language can help you with which aspects of data analysis? Select all that apply.
- Visualize your data
- Ask the right questions about your data
- Transform your data
- Clean your data
