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DSE - Data Science Data Visualization and Communication Flashcards

6 cards from real DSE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. Which Python library is primarily used for creating interactive, web-embeddable visualizations?

    Answer: Plotly

    Plotly generates interactive HTML/JavaScript charts that support zooming, hovering, and embedding, making it ideal for web-based data applications.

  2. What chart type uses color as a third dimension to represent a third variable across a 2D scatter plot?

    Answer: Bubble chart

    A bubble chart extends a scatter plot by varying the size or color of each point to encode a third variable, effectively visualizing three dimensions.

  3. What is the main advantage of using a logarithmic scale on a chart axis?

    Answer: It compresses large ranges so multiplicative relationships and skewed data are easier to interpret

    A logarithmic scale compresses wide numeric ranges, making exponential growth, power-law distributions, and data spanning multiple orders of magnitude readable on a single axis.

  4. Which visualization technique is specifically designed to display hierarchical or part-of-a-whole data using nested rectangles?

    Answer: Treemap

    A treemap uses nested rectangles whose areas are proportional to a quantitative variable, effectively visualizing hierarchical data and relative sizes simultaneously.

  5. In a ROC curve for a binary classifier, what two rates are plotted on the axes?

    Answer: True Positive Rate vs. False Positive Rate

    A ROC curve plots the True Positive Rate (sensitivity) on the Y-axis against the False Positive Rate (1-specificity) on the X-axis across all classification thresholds.

  6. Which type of map visualization is best suited for showing a continuous variable (e.g., population density) distributed across geographic regions?

    Answer: Choropleth map

    A choropleth map shades geographic regions by intensity of a variable, making spatial patterns in continuous data (like income or population density) immediately visible.