Google Data Analytics Professional Certificate Share Data Through the Art of Visualization Syllabus

Share Data Through the Art of Visualization syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Share Data Through the Art of Visualization in Google Data Analytics Professional Certificate, not a summary of it.

1. 1

   Visualizing Data

   3 topics

   • Principles of effective visualization
   • Data visualization design
   • Correlation versus causation
2. 2

   Creating Data Visualizations with Tableau

   3 topics

   • Getting started with Tableau
   • Building charts and graphs
   • Creating dashboards
3. 3

   Crafting Data Stories

   3 topics

   • Principles of data storytelling
   • Connecting data to objectives
   • Engaging your audience
4. 4

   Developing Presentations and Slideshows

   3 topics

   • Building effective presentations
   • Anticipating questions and feedback
   • Delivering data insights

Share Data Through the Art of Visualization flashcards for Google Data Analytics Professional Certificate

18 of 50 cards from the Share Data Through the Art of Visualization deck — real questions with worked answers.

1. According to the Google Data Analytics course, what is a data visualization?

   A graphical representation of information — such as a chart, graph, map, or dashboard — that makes data easier to understand, spot patterns in, and communicate to an audience.

2. What is the "five-second rule" for effective data visualizations?

   A visualization should let a viewer understand (1) what it is about within the first 5 seconds and (2) the key takeaway or conclusion within the next 5 seconds.

3. Name the four key elements of McCandless's framework for good data visualization.

   1) Information (the data), 2) Story (a clear, compelling narrative or concept), 3) Goal (a specific objective or function), 4) Visual form (effective use of metaphor and design). All four together produce successful visualizations.

4. In Kaiser Fung's Junk Charts Trifecta Checkup, what three questions should you ask about a visualization?

   1) What is the practical question? 2) What does the data say? 3) What does the visual say? A good chart aligns all three.

5. What are pre-attentive attributes in data visualization?

   Visual elements the brain processes automatically and unconsciously — such as color, size, shape, position, and line length — used to draw attention to the most important parts of a visualization. They are divided into marks and channels.

6. In visualization design, what is the difference between marks and channels?

   Marks are basic visual objects that represent data (points, lines, shapes/areas). Channels are visual properties of those marks (position, size, color) that encode the data's characteristics and vary in accuracy, popularity, and expressiveness.

7. What are the four main elements of art commonly applied to data visualization design?

   Line, shape, color, and space (movement is sometimes added as a fifth). Each should be used deliberately to organize information and guide the viewer's eye.

8. What three components describe color in visualization design?

   Hue (the color itself, e.g., red vs. blue), intensity/saturation (how vivid or dull the color is), and value (how light or dark the color is).

9. What are the nine core design principles for creating effective visualizations listed in the Google Data Analytics course?

   Balance, emphasis, movement, pattern, repetition, proportion, rhythm, variety, and unity.

10. In visualization design, what does the principle of "emphasis" mean?

    Making the key data or insight stand out — using color, size, or placement so the most important information draws the viewer's attention first.

11. What is a correlation in data analytics?

    A measure of the degree to which two variables move in relation to each other — a statistical relationship or association between two variables.

12. What is causation in data analytics?

    A relationship in which an action or event directly leads to (causes) another event — one event is the direct result of the other.

13. Why does correlation not imply causation?

    Two variables can be associated without one causing the other — the relationship may be coincidental or driven by a hidden third (confounding) variable. Example: ice cream sales and drowning deaths both rise in summer because of hot weather, not because one causes the other.

14. Give the classic example of a confounding variable that creates a spurious correlation between ice cream sales and drowning incidents.

    Hot summer weather — it increases both ice cream sales and swimming (hence drownings), producing a correlation between the two even though neither causes the other.

15. What is Tableau, and why is it used in data analytics?

    A business intelligence and data visualization platform that lets analysts connect to many data sources and create interactive charts, graphs, maps, and dashboards using a drag-and-drop interface — without programming.

16. What is Tableau Public?

    The free version of Tableau that lets anyone create and share interactive visualizations online; all visualizations ("vizzes") saved to it are publicly available.

17. In Tableau, what is the difference between a dimension and a measure?

    A dimension is a qualitative/categorical field (e.g., name, date, region) used to segment data; a measure is a quantitative, numeric field (e.g., sales, profit) that can be aggregated (summed, averaged).

18. In Tableau, what is a worksheet versus a dashboard?

    A worksheet contains a single view (one chart or graph) built from a data source; a dashboard is a collection of multiple worksheets/views combined on one screen for simultaneous comparison and monitoring.

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