Overview
Share Data Through the Art of Visualization is the sixth required course and covers the share phase of the analysis process. It teaches how to match a chart to the message (line for change over time, bar for comparing categories, scatter for relationships, histogram for distributions), how pre-attentive attributes such as position, size, shape and color direct the eye, and how design thinking and accessibility make visuals work for every viewer. It then introduces Tableau for building charts, linking datasets, and creating dashboards with filters.
The last two modules move from charts to communication: structuring a data story with a clear narrative, then presenting it, anticipating questions, handling objections and stating the caveats and limitations of the data. The course turns the numbers from Course 5 into something stakeholders can understand and act on.
How it's tested
Course 6 module challenges are mostly judgment items. You are given a message or dataset and choose the chart, a design flaw and the fix, a Tableau feature, or the best presentation response. The correct answer is the one that makes the key insight clearest for the stated audience while staying accurate and accessible.
-
Choose the chart
A message such as 'monthly sign-ups over two years' or 'share of budget by department' is described and you pick the chart type that shows it most clearly.
-
Fix the design
A visualization with a problem, such as relying only on red and green, a truncated axis or too many colors, is described and you choose the improvement.
-
Present and respond
A presentation moment is described, such as a stakeholder challenging the data, and you pick the response that addresses the concern honestly.
A scenario says an analyst must show how a subscription service's monthly cancellations changed over 24 months, and the question asks for the best chart. Candidates pick a pie chart because they see a share of customers. The message is change over time across many ordered periods, which a line chart shows directly. A pie chart compares parts of a single whole at one moment and cannot show a trend.
Modules 4
Official modules from the Google outline, in outline order.
6.1 Visualize dataChart types, effective design, accessibility and design thinking.
- What it means
- A data visualization is a graphical representation of data. This module covers chart types, the qualities of an effective visual, pre-attentive attributes, accessibility and design thinking.
- What to understand
- Pre-attentive attributes are what the eye notices before conscious thought; Google describes them as marks (position, size, shape, color) and channels (judged by accuracy, popout and grouping). Design thinking for visuals follows empathize, define, ideate, prototype and test. Accessible visuals label clearly, provide text alternatives, avoid relying on color alone and keep the design simple.
- How it shows up
- A message or flawed chart is described and you choose the chart type or the design change that makes the insight clear and accessible.
- Common confusion
- Treating marks and channels as the same; marks are visual objects such as points and bars, while channels are the visual properties used to encode data.
6.2 Create data visualizations with TableauUsing Tableau to build clear visuals and link datasets.
- What it means
- Tableau is a business intelligence and analytics platform for building interactive visuals; the course uses Tableau Public.
- What to understand
- Tableau separates dimensions (qualitative fields used to categorize, such as region) from measures (quantitative fields that can be aggregated, such as sales). Diverging color palettes show values above and below a meaningful midpoint, while sequential palettes show low-to-high values. Multiple data sources can be linked so fields from both can be used in one view.
- How it shows up
- A Tableau task is described and you choose the right field type, color palette or way to combine datasets.
- Common confusion
- Using a diverging palette for data with no meaningful midpoint, such as total sales, which suits a sequential palette.
6.3 Craft data storiesData-driven storytelling and Tableau dashboards and filters.
- What it means
- Data storytelling communicates a meaningful narrative based on data, combining engaging visuals with a clear message for a specific audience. Tableau dashboards bring several visuals together, and filters let viewers focus on what matters to them.
- What to understand
- A data story has characters (the people affected), a setting, a plot that builds tension, a big reveal (the key insight) and an 'aha' moment that leads to action. Spotlighting means scanning notes or observations for recurring themes to find the story. Dashboard filters should help the audience answer their questions without overwhelming them.
- How it shows up
- You are given raw findings and an audience and choose the element missing from the story, or the dashboard design that helps that audience.
- Common confusion
- Presenting every chart produced during analysis; a story shows only the visuals that build toward the insight.
6.4 Develop presentations and slideshowsEffective presentations, anticipating questions and explaining data limitations.
- What it means
- This module covers structuring a data presentation, delivering it, responding to questions and objections, and being explicit about caveats and limitations.
- What to understand
- Good presentations start with the business task and key takeaway, keep each slide focused on one point, and pause after showing a visual so the audience can absorb it before you explain it. Objections usually concern the data, the analysis or the findings, and the response is to listen, restate the question, answer honestly and follow up if needed.
- How it shows up
- A stakeholder raises a concern during a presentation, or the data has a known limit, and you choose the best way to respond or disclose it.
- Common confusion
- Treating caveats as a sign of weak work; stating limitations builds trust and prevents the audience from overreaching.
High-yield concepts
The terms, rules and formulas this domain turns into questions most often.
-
Chart selection
Line charts show change over time, bar and column charts compare categories, pie charts show parts of one whole, scatter plots show relationships between two variables and histograms show distributions.
- Remember
- Choose the chart from the message, not from what looks impressive.
- Tested as
- The most common Module 1 item.
- Trap
- Pie charts with many slices or for comparisons across time.
-
Pre-attentive attributes
Visual features the eye notices before conscious thought, used to draw attention to what matters.
- Remember
- Marks are position, size, shape and color; channels are judged by accuracy, popout and grouping.
- Tested as
- Design vocabulary items.
- Trap
- Using strong color everywhere so nothing pops out.
-
McCandless method
A presentation approach: introduce the graphic by name, answer obvious questions before they are asked, state the insight, call out the data that supports it, and tell the key takeaway.
- Remember
- It also frames effective visuals as combining information, story, goal and visual form.
- Tested as
- Presentation and design items.
- Trap
- Showing the chart and expecting the audience to find the insight.
-
Design thinking
A process for creating visuals that work for the audience: empathize, define, ideate, prototype and test.
- Remember
- Empathize comes first: understand who will use the visual and what they need.
- Tested as
- Module 1 process items.
- Trap
- Starting with the chart before defining the audience's need.
-
Accessibility
Making visuals usable by people with different abilities, such as color vision deficiency or low vision.
- Remember
- Label directly, add text alternatives, do not rely on color alone and simplify.
- Tested as
- Design-fix items.
- Trap
- Encoding meaning only in red versus green.
-
Static vs. dynamic visualizations
Static visuals do not change once created; dynamic or interactive visuals let viewers filter and explore.
- Remember
- Static gives you control of the message; dynamic gives the audience flexibility.
- Tested as
- Choosing a format for an audience.
- Trap
- Assuming interactive is always better.
-
Dimensions vs. measures
In Tableau, dimensions are qualitative fields that categorize data and measures are quantitative fields that can be aggregated.
- Remember
- Region and product are dimensions; sales and quantity are measures.
- Tested as
- Tableau items in Module 2.
- Trap
- Treating a numeric ID as a measure and summing it.
-
Diverging vs. sequential palette
Diverging palettes use two hues meeting at a midpoint; sequential palettes run from light to dark in one hue.
- Remember
- Use diverging when values above and below a reference matter, such as profit and loss.
- Tested as
- Color-choice items.
- Trap
- Applying diverging color to data with no meaningful center.
-
Data storytelling elements
Characters, setting, plot, big reveal and an 'aha' moment structure a data story.
- Remember
- Every visual in the story should move toward the big reveal.
- Tested as
- Module 3 story items.
- Trap
- Including every chart from the analysis.
-
Spotlighting
Scanning notes or observations for recurring ideas to find the key themes of a story.
- Remember
- It helps turn many findings into one clear narrative.
- Tested as
- Story-building items.
- Trap
- Picking the most surprising finding instead of the most recurring theme.
-
Caveats and limitations
Statements of what the data cannot show, such as a short time frame, missing groups or correlation without causation.
- Remember
- Disclose them during the presentation, close to the finding they qualify.
- Tested as
- Module 4 items.
- Trap
- Leaving limitations out to make findings sound stronger.
-
Handling objections
Objections usually target the data, the analysis or the findings.
- Remember
- Listen, restate the question, answer honestly and follow up if you need to check.
- Tested as
- Presentation scenario items.
- Trap
- Defending the work before understanding the concern.
Side by side
Concepts candidates mix up, compared on the point that decides the answer.
Choosing a chart
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Line chart | Plots values across ordered time periods | Shows trends and change over time | Using it for unordered categories |
| Bar or column chart | Compares values across categories | Best for comparing sizes | Starting the value axis above zero and exaggerating differences |
| Pie chart | Shows parts of a single whole | Only for a few categories at one point in time | Using many slices or comparing several pies |
| Scatter plot | Plots two numeric variables against each other | Shows relationships and correlation | Reading correlation as causation |
| Histogram | Shows how values are distributed across bins | Displays frequency of ranges | Treating it as a bar chart of categories |
Dimensions vs. measures in Tableau
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Dimension | Qualitative field such as region, product or date part | Used to slice and categorize | Aggregating it |
| Measure | Quantitative field such as sales or quantity | Aggregated with sum, average or count | Treating ID numbers as measures |
Static vs. dynamic visualizations
| Concept | Meaning | Key difference | Common trap |
|---|---|---|---|
| Static | Fixed image or slide | Analyst controls the message | Using it when stakeholders need to explore |
| Dynamic | Interactive dashboard with filters | Audience explores the data | Giving it to an audience that needs one clear answer |
Exam traps
Each trap is an answer that looks right. Learn the clue that should change your mind.
-
Pie chart for a trend
- What candidates pick
- A pie chart to show how market share changed over five years.
- Why it looks right
- Market share is a part-of-whole idea.
- Why it's wrong
- A pie shows one whole at one moment; change over time needs a line chart or a stacked bar chart per year.
Clue to watch for A time dimension with several periods.
-
Color alone
- What candidates pick
- Red and green fills to mark on-target and off-target stores.
- Why it looks right
- Red and green are intuitive for good and bad.
- Why it's wrong
- People with red-green color vision deficiency cannot tell them apart; add labels, icons or a different contrast.
Clue to watch for The question mentions accessibility or a broad audience.
-
More visuals, more value
- What candidates pick
- Show all twelve charts produced during analysis.
- Why it looks right
- It shows thoroughness.
- Why it's wrong
- A data story uses only the visuals that build to the insight; extra charts bury the message.
Clue to watch for A short meeting or an executive audience.
-
Diverging palette by default
- What candidates pick
- A red-to-blue diverging palette for total sales by state.
- Why it looks right
- Two-hue palettes look striking.
- Why it's wrong
- Diverging palettes imply a meaningful midpoint; total sales runs low to high and suits a sequential palette.
Clue to watch for Whether the data has a natural center such as zero, a target or an average.
-
Defend first
- What candidates pick
- Immediately explain why the stakeholder's objection is wrong.
- Why it looks right
- It shows confidence in the work.
- Why it's wrong
- The course teaches listening and restating the concern first, then answering honestly or following up after checking.
Clue to watch for A stakeholder questions the data or method mid-presentation.
-
Hide the limitation
- What candidates pick
- Leave out that the survey covered only one region.
- Why it looks right
- It keeps the message simple and confident.
- Why it's wrong
- Undisclosed limits lead stakeholders to overreach; caveats should be stated with the finding they affect.
Clue to watch for The scenario mentions a known gap in time, place or population.
Answer framework
Use these steps when a Course 6 item asks how to show or present a finding.
- Identify the audience and what decision they need to make, as design thinking's empathize step requires.
- State the single message in one sentence, then pick the chart that shows that message type: trend, comparison, part of whole, relationship or distribution.
- Use pre-attentive attributes to make the key point pop, and check accessibility by not relying on color alone.
- Build the story with only the visuals that lead to the big reveal, choosing static or interactive formats to suit the audience.
- Present with the takeaway first, state caveats beside the findings, and answer objections by listening, restating and responding honestly. Offer to follow up on any question you cannot answer on the spot.
Practice question
Want more like this? The Google Data Analytics practice test has questions for every domain.
Checklist
0 of 13
Tick each one you can do without notes. Your ticks stay in this browser.
Study strategy
Start with Module 1 and build a one-page chart chooser: for each message type, the right chart and the most common misuse. Then practice on real headlines by deciding which chart you would use before looking at the one published.
Give Modules 2 and 3 hands-on time in Tableau Public. Rebuild one dataset from Course 5 as three charts and a dashboard with a filter, deliberately trying sequential and diverging palettes and placing fields as dimensions and measures. Then write a five-slide story that uses only the visuals that lead to the insight.
The underestimated area is Module 4. Rehearse your story aloud using the McCandless steps, and prepare one answer each for objections to the data, the method and the findings. Write the caveats on the same slide as the finding they qualify. Afterward, take a practice set on chart choice and design fixes, then continue to Course 7, Introduction to Data Analysis Using Python.
FAQ
Which visualization tool does Course 6 teach?
Tableau, using Tableau Public. The course also covers presentation tools such as Google Slides or PowerPoint for delivering findings.
How is Course 6 assessed?
Each of the four modules ends with a graded module challenge, alongside knowledge checks, self-reflections and hands-on Tableau activities.
What are pre-attentive attributes?
Visual properties the eye notices before conscious thought. Google groups them into marks (position, size, shape, color) and channels, which are judged by accuracy, popout and grouping.
When should I use a pie chart?
Only to show parts of a single whole at one point in time with a few categories. For trends use a line chart, and for comparing many categories use a bar chart.
Should I mention limitations in a presentation?
Yes. The course teaches stating caveats and limitations alongside the findings they affect, so stakeholders do not draw conclusions the data cannot support.
How long should I plan for Course 6?
Coursera lists about 19 hours. Our editorial planning estimate is 25 to 35 hours, mostly for Tableau practice and rehearsing a presentation.
How does Course 6 connect to the capstone?
The capstone's share phase asks for clear visualizations and a coherent story supporting your recommendations, so the chart, design and storytelling skills here are applied directly.
Other Data & Analytics certifications
Where the same subject shows up in other exams. Cards marked "Same subject" open the matching domain.
Test your Domain 6 knowledge
You have covered chart choice, pre-attentive attributes, accessibility, Tableau, data storytelling and presenting caveats. Test whether you can pick the clearest visual and response for a given audience.
Keep going
- Training Training Built on your Weak areas
- Practice test Questions by domain, with explanations
- Mock exam Full length, timed like the real exam
- Readiness test Find your weakest domain in minutes
- Cheat sheet Key terms and formulas on one page
- Ask the Google Data Analytics AI tutor Get any concept explained your way