Data Science Data Visualization Syllabus

Data Visualization syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Data Visualization in Data Science, not a summary of it.

1. 1

   Introduction to Data Visualization

   3 topics

   • What is Data Visualization?
     • Definition and Importance
     • History of Data Visualization
   • Types of Data Visualization
     • Charts and Graphs
     • Maps
     • Infographics
     • Dashboards
   • Applications of Data Visualization
     • Business Intelligence
     • Scientific Research
     • Journalism
2. 2

   Principles of Data Visualization

   3 topics

   • Data Integrity
     • Accuracy
     • Consistency
   • Design Principles
     • Clarity
     • Efficiency
     • Aesthetics
   • Human Perception
     • Color Theory
     • Visual Hierarchy
     • Gestalt Principles
3. 3

   Tools and Technologies

   3 topics

   • Programming Languages
     • Python (Matplotlib, Seaborn, Plotly)
     • R (ggplot2, Shiny)
     • JavaScript (D3.js)
   • Software and Platforms
     • Tableau
     • Power BI
     • Google Data Studio
   • Database and Data Sources
     • SQL Databases
     • NoSQL Databases
     • APIs
4. 4

   Creating Effective Visualizations

   3 topics

   • Choosing the Right Chart
     • Bar Charts
     • Line Charts
     • Pie Charts
     • Scatter Plots
     • Heatmaps
   • Data Preprocessing
     • Data Cleaning
     • Data Transformation
     • Handling Missing Values
   • Annotation and Labeling
     • Titles and Captions
     • Axis Labels
     • Legends
5. 5

   Advanced Data Visualization Techniques

   3 topics

   • Interactive Visualizations
     • Hover and Click Events
     • Zoom and Pan
     • Dynamic Filtering
   • Geospatial Visualization
     • Choropleth Maps
     • Geocoding
     • Heat Maps
   • 3D Visualization
     • 3D Scatter Plots
     • Surface Plots
     • Volume Rendering
6. 6

   Case Studies and Real-World Applications

   3 topics

   • Business Analytics
     • Sales and Marketing
     • Financial Analysis
     • Customer Insights
   • Healthcare
     • Epidemiology
     • Clinical Data
     • Genomics
   • Public Policy
     • Census Data
     • Crime Statistics
     • Environmental Data
7. 7

   Ethics and Best Practices

   2 topics

   • Ethical Considerations
     • Data Privacy
     • Bias and Misrepresentation
   • Best Practices
     • Transparency
     • Reproducibility
     • Accessibility

Data Visualization flashcards for Data Science

22 of 51 cards from the Data Visualization deck — real questions with worked answers.

1. What is data visualization?

   The graphical representation of data and information using visual elements such as charts, graphs, maps, and plots, to make patterns, trends, outliers, and relationships easier to understand and communicate.

2. What are the two broad purposes of data visualization?

   Exploratory (to discover and understand patterns in data during analysis) and explanatory (to communicate already-understood findings to an audience).

3. What is the core difference between a chart, a graph, and a plot?

   They are largely interchangeable; loosely, a graph/plot emphasizes mathematical relationships between variables (e.g., scatter plot), while a chart is any visual presentation of data including non-coordinate forms (e.g., pie chart).

4. Name the main categories of data visualization by data relationship.

   Comparison, composition (part-to-whole), distribution, relationship (correlation), and trend over time.

5. Which chart types are best for showing distribution of a single variable?

   Histogram, box plot (box-and-whisker), violin plot, and density plot.

6. Which visualization is best for showing part-to-whole composition?

   Pie chart, stacked bar chart, stacked area chart, or treemap.

7. Which visualization best shows the relationship/correlation between two continuous variables?

   A scatter plot (with a bubble chart adding a third variable via point size).

8. Which visualization is best for showing a trend over time?

   A line chart (or area chart), with time on the x-axis.

9. What is a treemap used for?

   Displaying hierarchical part-to-whole data using nested rectangles whose area is proportional to a quantitative value.

10. What is a heatmap?

    A visualization that represents the magnitude of values in a matrix using color intensity, useful for spotting patterns, correlations, and density.

11. List three common applications of data visualization.

    Business analytics and dashboards, scientific/medical research, journalism and public policy communication, financial market analysis, and monitoring/operations (any three).

12. What is data integrity in the context of visualization?

    The accuracy, completeness, and consistency of data so that the resulting visualization truthfully represents reality without distortion or misleading manipulation.

13. What is the lie factor, as defined by Edward Tufte?

    A measure of distortion: $$\text{Lie Factor} = \frac{\text{size of effect shown in graphic}}{\text{size of effect in data}}$$ A truthful graphic has a lie factor of approximately $1$.

14. Why should bar chart axes generally start at zero?

    Because a bar's length encodes its value; truncating the axis exaggerates differences and inflates the lie factor, misleading the viewer.

15. What is the data-ink ratio (Tufte)?

    $$\text{Data-ink ratio} = \frac{\text{ink used to display data}}{\text{total ink used in the graphic}}$$ Tufte advises maximizing it by removing non-essential (non-data) ink.

16. What is 'chartjunk'?

    Tufte's term for unnecessary or decorative visual elements (3D effects, heavy gridlines, background images) that do not convey data and distract from it.

17. State the principle of proportional ink.

    The size of a shaded area in a graphic should be directly proportional to the data value it represents.

18. What are the Gestalt principles relevant to visualization design?

    Proximity, similarity, enclosure, closure, continuity, and connection — perceptual rules describing how humans group visual elements.

19. What is preattentive processing in human perception?

    The rapid (under ~250 ms), automatic detection of certain visual features such as color, length, orientation, and motion before conscious attention is applied.

20. Rank these encodings by perceptual accuracy (most to least accurate) per Cleveland and McGill.

    Position along a common scale > position on non-aligned scales > length > angle/slope > area > volume > color/saturation.

21. Why is encoding quantitative data by position more accurate than by area or color?

    Human visual perception judges position along a common scale most precisely, whereas area and color are judged with much greater error, making them suited only to approximate or categorical comparisons.

22. What is a colorblind-safe palette and why does it matter?

    A palette (e.g., avoiding red-green contrasts, or using ColorBrewer/viridis) chosen so that ~8% of men and ~0.5% of women with color vision deficiency can still distinguish encoded categories.

See more Data Visualization flashcards →

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