8.9.1: Review Questions

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Page ID: 58323

Felix Amoruwa
De Anza College

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Review Questions

Multiple Choice

Which Python library is best suited for creating interactive visualizations that can be embedded in web pages?
- a) Matplotlib b) Seaborn c) Plotly d) NumPy
A business analyst with no programming experience needs to create an interactive sales dashboard for executives. Which tool is most appropriate?
- a) Matplotlib in Jupyter b) Altair c) Tableau or Power BI d) A custom D3.js application
Which statement about Seaborn is correct?
- a) It replaces Matplotlib entirely b) It is built on Matplotlib and provides higher-level statistical plot functions c) It only creates interactive charts d) It requires a commercial license

Short Answer

Compare Matplotlib and Seaborn. When would you choose Matplotlib over Seaborn, and vice versa? Give a specific use case for each.
What are the key advantages and disadvantages of commercial BI tools (Tableau, Power BI) compared to open-source Python libraries?
Describe a workflow where an analyst uses both Python and Tableau. What role does each tool play?
Compare Plotly and Altair for creating interactive visualizations. What are the strengths and limitations of each?

True/False

True or False: Tableau requires users to write code to create visualizations.
True or False: Power BI's free tier allows individual users to create reports but has limitations on sharing and collaboration.
True or False: Matplotlib is the only Python library capable of producing publication-quality static charts.

Answer Key:

1-c, 2-c, 3-b, 4. Matplotlib: when you need complete control over every element (publication figures, custom chart types). Seaborn: when you want attractive statistical plots quickly (box plots, violin plots, heatmaps with minimal code). 5. Commercial: easier for non-coders, polished dashboards, enterprise sharing, vendor support. Drawbacks: licensing cost, less customization. Open-source: free, fully customizable, integrates with data science workflows. Drawbacks: requires coding, less polished sharing. 6. Python (Jupyter): data cleaning, EDA, statistical modeling, creating detailed analytical plots. Tableau: building polished, interactive dashboards from the cleaned/analyzed data for business stakeholders. 7. Plotly: rich interactivity, many chart types (3D, maps), good for dashboards (Dash framework), larger community. Altair: more concise declarative syntax, easier for quick exploration, but limited customization and 5000-row default limit. 8. False, 9. True, 10. False (Seaborn and Plotly can also produce high-quality static exports).

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