Why do data analysts use alternative text to make their data visualization more accessible?

Accessible visualizations

Being clear with text, distinctive labeling, and adding multiple ways to identify the point to your visuals will make it easier for people with impairments and those without to interpret your graphs. There are easy ways to add the principles of accessibility into your visual communications. Here are five simple ones.
 

1. Add Alt text

Alternative text (referred to as Alt text) is displayed when the image cannot be. Screen readers, the assistive technology used by people who are visually impaired, read alt text out loud in place of people seeing the image. It’s important to have valuable alt text instead of “figure-13.jpg,” which doesn’t help a user understand the content they are missing. Screen readers speak alt text without allowing users to speed up or skip, so make sure the information is descriptive but succinct.

According to the CFPB data visualization guide, which I helped create, good alt text includes: one sentence of what the chart is, including the chart type for users with limited vision who may only see part of it. It should also include a link to a CSV or other machine-readable data format so people with impaired vision can tab through the chart data with a screen reader.

If you frequently use data visualizations in Microsoft products, this guide will help you learn how to add alt text to them.
 

2. Employ a takeaway title

Research suggests that users read the title of the graph first. People also tend to just rephrase the title of graph when asked to interpret the meaning of the visualization. When the graph title includes the point, the cognitive load of understanding the chart decreases. Recently, when writing about how to effectively use words in graphs, Cole advocated for what she called a takeaway title. People know what to look for in the data when they read the graph takeaway first as part of the title.
 

3. Label data directly

One way to reduce the cognitive burden on users it to directly label your data rather than using legends. This is especially useful for colorblind or visually impaired users who may have difficulty matching colors within the plot to those in the legend. It also decreases the work of scanning back and forth trying to match the legend with the data.

Notice the difference in the following visual example. Compare how quickly you understand them and the relative ease of processing the one that is labeled directly.

Coursera Google Data Analytics Professional Certificate Course 6: Share Data Through the Art of Visualization – Visualize Data quiz answers to all weekly questions (weeks 1 – 4):

• Week 1: Visualizing data
• Week 2: Creating data visualizations with Tableau
• Week 3: Crafting data stories
• Week 4: Developing presentations and slideshows

You may also be interested in Google Data Analytics Professional Certificate Course 1: Foundations – Cliffs Notes.

Week 1: Visualizing data

Data visualization is the graphical representation of data. In this part of the course, you’ll be introduced to key concepts, including accessibility, design thinking, and other factors that play a role in visualizing the data in your analysis.

Learning Objectives

• Explain the key concepts involved in design thinking as they relate to data visualization
• Describe the use of data visualizations to talk about data and the results of data analysis
• Discuss accessibility issues associated with data visualization
• Explain the importance of data visualization to data analysts
• Describe the key concepts involved in data visualization

Answers to week 1 quiz questions

L2 Data visualization

Question 1

Fill in the blank: Correlation charts show _ among data.

• relationships
• causation
• changes
• outcomes

Correlation charts show relationships among data.

Question 2

After analyzing their data, a junior analyst creates bar graphs, line graphs, and pie charts to help explain findings to stakeholders. These are all examples of what?

• Data visualizations
• Data filters
• Data tabulations
• Data transformations

Bar graphs, line graphs, and pie charts are all examples of data visualizations.

Question 3

When does causation, or a cause-effect relationship, occur?

• When an action could potentially lead to different outcomes
• When an outcome could have been caused by multiple actions
• When an action could have affected an outcome
• When an action directly leads to an outcome

Causation occurs when an action directly leads to an outcome. Causation indicates a clear cause and effect relationship between variables.

Question 4

Which of the following are part of McCandless’s elements of effective data visualization? Select all that apply.

• The goal
• The structure
• The moral
• The visual form

There are four elements of effective data visualization according to David McCandless. These include the information, the story, the goal, and the visual form.

L3 Designing data visualizations

Question 1

Which element of design can add visual form to your data and help build the structure for your visualization?

Lines add visual form to your data and help build the structure for your visualization.

Question 2

Which of the following are elements for effective visuals? Select all that apply.

• Sophisticated use of contrast
• Clear goal
• Clear meaning
• Refined execution

The elements for effective visuals are clear meaning, sophisticated use of contrast, and refined execution.

Question 3

Fill in the blank: Design thinking is a process used to solve complex problems in a _ way.

• action-oriented
• step-by-step
• user-centric
• pre-attentive

Design thinking is a process used to solve complex problems in a user-centric way. It enables data analysts to identify alternative strategies for visualizations.

Question 4

While creating a data visualization for your stakeholders, you realize certain colors might make it harder for your audience to understand your data. What phase of the design process does this represent?

• Define
• Empathize
• Ideate
• Prototype

Considering appropriate colors for a visualization is part of the empathize design phase. During the empathize phase, you consider the emotions and needs of the target audience for your data visualization.

L4 Explore visualization considerations

Question 1

What are the three basic visualization considerations? Select all that apply.

• Headlines
• Subtitles
• Labels
• Text

The three basic visualization considerations are headlines, subtitles, and labels. These all include text, but are formatted differently for different purposes.

Question 2

Directly labeling a data visualization helps viewers identify data more efficiently. Legends are often less effective because they are positioned away from the data.

Directly labeling a data visualization helps viewers identify data quickly. Legends are often less effective because they are positioned away from the data.

Question 3

In what ways can data analysts use alternative text to make their data visualizations more accessible?

• To make the presentation of data clearer
• To provide a textual alternative to non-text content
• To make data visualizations easier to read
• To add context to the data visualization

Alternative text provides a textual alternative to non-text context. Alternative text ensures that users who need to access your data visualizations in different ways, like with a screen-reader, will still absorb the information.

Question 4

You are creating a data visualization and want to ensure it is accessible. What strategies do you use to simplify the visual? Select all that apply.

• Do not create overly complicated charts
• Do not include long chunks of text
• Do not include labels
• Do not include too much information

Simplifying your data visualizations can help your audience understand and focus on the important data. To do this, avoid overly complicated visuals or unnecessary information.

Weekly challenge 1

Question 1

A data analyst wants to create a visualization that demonstrates how often data values fall into certain ranges. What type of data visualization should they use?

• Scatter plot
• Histogram
• Correlation chart
• Line graph

To demonstrate how often data values fall into certain ranges, the data analyst should use a histogram.

Question 2

A data analyst notices that two variables in their data seem to rise and fall at the same time. They recognize that these variables are related somehow. What is this an example of?

• Causation
• Tabulation
• Visualization
• Correlation

When a data analyst notices that two variables rise and fall at the same time, this is an example of correlation. Correlation is the measure of the degree to which two variables change in relationship to each other.

Question 3

Fill in the blank: A data analyst creates a presentation for stakeholders. They include _ visualizations because they want them to be interactive and automatically change over time.

• geometric
• aesthetic
• dynamic
• static

They include dynamic visualizations. Dynamic visualizations are interactive and can automatically change over time.

Question 4

What are the key elements of effective visualizations you should focus on when creating data visualizations? Select all that apply.

• Sophisticated use of contrast
• Refined execution
• Visual form
• Clear meaning

The elements for effective visualization are clear meaning, sophisticated use of contrast, and refined execution.

Question 5

Fill in the blank: Design thinking is a process used to solve problems in a _ way.

• critical
• design-centric
• analytical
• user-centric

Design thinking is a process used to solve complex problems in a user-centric way.

Question 6

You are in the ideate phase of the design process. What are you doing at this stage?

• Generating visualization ideas
• Sharing data visualizations with a test audience
• Making changes to their data visualization
• Creating data visualizations

There are five phases of the design process: empathize, define, ideate, prototype, and test. The ideate phase is when you start to generate your data visualization ideas.

Question 7

A data analyst wants to make their visualizations more accessible by adding text explanations directly on the visualization. What is this called?

• Labeling
• Subtitling
• Simplifying
• Distinguishing

This is labeling. Labeling data directly instead of relying on legends can make data visualizations more accessible.

Question 8

Distinguishing elements of your data visualizations makes the content easier to see. This can help make them more accessible for audience members with visual impairments. What are some methods data analysts use to distinguish elements?

• Add a legend
• Ensure all elements are highlighted equally
• Separate the foreground and background
• Use contrasting colors and shapes

Data analysts distinguish elements of data visualizations by separating the foreground and background and using contrasting colors and shapes.

Week 2: Creating data visualizations with Tableau

Tableau is a tool that helps data analysts create effective data visualizations. In this part of the course, you’ll learn all about Tableau and explore the importance of creativity and clarity while visualizing your data analysis findings.

Learning Objectives

• Identify Tableau as a data visualization tool and understand its uses
• Explain how data visualization can allow for creativity and clarity to appropriately present findings
• Select appropriate visuals for various presentation situations
• Identify different types of data visualizations and their uses
• Use multiple data sources to create a visualization
• Discuss accessibility issues associated with data visualization

Answers to week 2 quiz questions

L2 Getting started with Tableau

Question 1

As a business intelligence and analytics platform, Tableau enables you to do what with data? Select all that apply.

• Connect to data in databases, spreadsheets, or CSV files
• Observe and understand data to make decisions
• Create and share interactive dashboards with data
• Check and clean data in databases

Tableau enables you to observe and understand data to make decisions, connect to data in databases, spreadsheets, or CSV files, and create and share interactive dashboards with data.

Question 2

You are comparing Tableau to Looker and Google Data Studio for your company’s data visualization needs. What feature is unique to Tableau?

• Drag and drop functionality to create visualizations
• Integration of multiple data sources
• Desktop version for users
• Connectivity to SQL databases

Tableau offers browser-based and desktop versions while Looker and Google Data Studio are strictly browser-based. Browser-based solutions are preferred by companies adopting cloud-based services while desktop solutions might be suitable for companies maintaining services on their private networks.

Question 3

Fill in the blank: When using Tableau Public, click the Gallery tab to access _.

• public visualizations
• how-to videos
• blog articles
• sample data

Tableau Public’s Gallery features data visualization examples created by other Tableau Public users across the web.

L3 Create visualizations in Tableau

Question 1

A diverging color palette in Tableau displays characteristics of values using what color combination?

• Shade for the accuracy and grayscale for the reliability
• Hue for the range and tint for the margin of error
• Intensity for the magnitude and hue for the range
• Intensity for the range and hue for the magnitude

A diverging color palette in Tableau displays a value’s magnitude by color intensity and a value’s range by color hue. The color palette isn’t used to distinguish the accuracy or reliability of data, but you should always check that the values you use in a visualization are both accurate and reliable.

Question 2

A data analyst creates a Tableau visualization to compare the trade (amount of goods and services exchanged) between the European Union (EU) and Australia. Which color choice could be misleading?

• Green for the EU and red for Australia
• Blue for the EU and gray for Australia
• Beige for the EU and purple for Australia
• Orange for the EU and brown for Australia

A lot of people associate green with positive results and red with negative results. Green could falsely represent a trade surplus for the EU and red could falsely represent a trade deficit for Australia. Selecting beige and purple wouldn’t lead people to these wrong conclusions.

Question 3

How could you adjust the labels to make the following visualization more effective? Select all that apply.

Each country has statistics for family, health, freedom, and generosity

• Move the labels to white space on the map
• Reduce the number of labels
• Change the font color for the labels from black to white
• Use a single font for the labels

You could make the visualization more effective by reducing the number of labels per country and using only one font. Doing this makes the labels easier to read.

Weekly challenge 2

Question 1

Fill in the blank: When using Tableau, people can control what data they see in a visualization. This is an example of Tableau being _.

• interpretive
• interactive
• indefinable
• inanimate

People being able to control what data they see is an example of Tableau being interactive.

Question 2

A data analyst is using the Color tool in Tableau to apply a color scheme to a data visualization. They want the visualization to be accessible for people with color vision deficiencies, so they use a color scheme with lots of contrast. What does it mean to have contrast?

• The color scheme uses a range of different colors
• The color scheme is graphically pleasing
• The color scheme is monotone
• The color scheme is uniform

The data analyst makes sure the color scheme has contrast in order to make the visualization accessible for people with color vision deficiencies.

Question 3

What could a data analyst do with the Lasso tool in Tableau?

• Select a data point
• Zoom in on a data point
• Move a data point
• Pan across data points

A data analyst could use the Lasso tool to select a data point.

Question 4

A data analyst is using the Pan tool in Tableau. What are they doing?

• Moving a data point to another location in the visualization
• Rotating the perspective while keeping a certain object in view
• Deselecting a data point from within the visualization
• Taking a screenshot of the visualization

They are using the Pan tool to rotate the perspective while keeping a certain object in view.

Question 5

You are working with the World Happiness data in Tableau. To display the population of each country on the map, which Marks shelf tool do you use?

To display the population of each country on the map, you use the Label property.

Question 6

When working with the World Happiness data in Tableau, what could you use the Filter tool to do?

• Show only countries with a World Happiness score of 3.5 or lower
• Permanently delete countries without a happiness score
• Reformat every country in Asia
• Zoom out to reveal the entire world

You could use the Filter tool to show only those countries with a World Happiness score of 3.5 or lower.

Question 7

By default, all visualizations you create using Tableau Public are available to other users. What icon to you click to hide a visualization?

To hide a visualization from other users, click the Eye icon.

Question 8

Fill in the blank: In Tableau, a _ palette displays two ranges of values. It uses a color to show the range where a data point is from and color intensity to show its magnitude.

• diverging
• overlaying
• inverting
• contrasting

In Tableau, a diverging palette displays two ranges of values. It uses a color to show the range where a data point is from and color intensity to show its magnitude.

Week 3: Crafting data stories

Connecting your objective with your data through insights is essential to good data storytelling. In this part of the course, you’ll learn about data-driven stories and their attributes. You’ll also gain an understanding of how to use Tableau to create dashboards and dashboard filters.

Learning Objectives

• Explain data-driven stories, including reference to their importance and their attributes
• Demonstrate an understanding of how to use Tableau to create dashboards and dashboard filters
• Explain how data stories can be used in different forms of on-the-job communication

Answers to week 3 quiz questions

L2 Data-driven stories

Question 1

Data storytelling involves which of the following elements? Select all that apply.

• Describing the steps of your analysis process
• Communicating the meaning of a dataset with visuals
• Using a narrative that is customized to your audience
• Selecting only the data points that support your case

Data storytelling involves communicating the meaning of a dataset with visuals and using a narrative that is customized to your audience.

Question 2

A data analyst presents their data story to an audience. They aim to capture and hold the audience members’ interest and attention. Which data storytelling concept does this describe?

• Narrative
• Primary message
• Visuals
• Engagement

Engagement involves capturing and holding the audience members’ interest and attention.

Question 3

Which of the following activities would a data analyst do while spotlighting? Select all that apply.

• Focus on the details of the analysis and results
• Write notes on a white board that contain the data analysis insights
• Search for broad, universal ideas and messages
• Identify ideas or concepts that arise repeatedly

Spotlighting involves scanning through data to quickly identify the most important insights. This can be done with notes on a whiteboard, by searching for broad ideas, and by identifying concepts that arise repeatedly.

L3 Use Tableau dashboard

Question 1

Fill in the blank: A dashboard organizes information from multiple datasets into one central location. This enables the information to be _. Select all that apply.

• visualized
• protected
• tracked
• analyzed

A dashboard is used to track, analyze, and visualize information.
You didn’t select all the correct answers

Question 2

A data analyst is choosing their Tableau dashboard layout. They want the layout to automatically resize itself based on the dashboard size. They should use a tiled layout.

To automatically resize the layout based on the dashboard size, the analyst should use a tiled layout.

L4 Communicate data stories

Question 1

A new challenge from a competitor, an inefficient process that needs to be improved, or a potential business opportunity could all represent which aspect of data storytelling?

• Plot
• Big reveal
• Setting
• Aha moment

A new challenge from a competitor, an inefficient process that needs to be improved, or a potential business opportunity could all be a plot in the data story. The plot reveals the problem and compels the characters to act. The big reveal is when the data shows how the problem can be solved.

Question 2

Fill in the blank: When designing a presentation, a slideshow tool called _ can be used to control the color, font types and sizes, formating, and positioning of text and visuals.

• themes
• patterns
• schemes
• motives

When designing a presentation, themes can be used to control the color, font types and sizes, formating, and positioning of text and visuals.

Question 3

A data analyst includes a visual in their presentation to represent information from a dataset. It’s important that the visual reflect the latest information, so the analyst wants it to update automatically if the original dataset changes. The analyst should copy and paste the visual into the presentation.

They should link the visual to its original file. Copying and pasting a visual into a presentation means it won’t be updated if the original dataset changes. This means the visual might not reflect the latest information.

Weekly challenge 3

Question 1

Engaging your audience, creating compelling visuals, and using an interesting narrative are all part of what practice?

• Data composition
• Data design
• Data strategy
• Data storytelling

Engaging your audience, creating compelling visuals, and using an interesting narrative are all part of data storytelling.

Question 2

A data analyst wants to communicate to others about their analysis. They ensure the communication has a beginning, a middle, and an end. Then, they confirm that it clearly explains important insights from their analysis. What aspect of data storytelling does this scenario describe?

• Takeaways
• Narrative
• Spotlighting
• Setting

This scenario describes the data storytelling narrative. An effective narrative has a beginning, a middle, and an end. It also clearly explains important insights from the analysis.

Question 3

You are preparing to communicate to an audience about an analysis project. You consider the roles that your audience members play and their stake in the project. What aspect of data storytelling does this scenario describe?

• Engagement
• Theme
• Discussion
• Takeaways

Considering the roles your audience members play and their stake in the project describes audience engagement. Engagement is capturing and holding someone’s interest and attention.

Question 4

When designing a dashboard, how can data analysts ensure that charts and graphs are most effective? Select all that apply.

• Include as many visual elements as possible
• Incorporate all of the data points from the analysis
• Make good use of available space
• Place them in a balanced layout

When designing a dashboard, data analysts can ensure that charts and graphs are most effective by placing them in a balanced layout and making good use of available space.

Question 5

A data analyst is creating a dashboard using Tableau. In order to layer objects over other items, which layout should they choose?

• Tiled
• Floating
• Itemized
• Layered

In order to layer objects over other items in a Tableau dashboard, they should choose a floating layout. Floating items can be layered over other objects.

Question 6

Which of the following are appropriate uses for filters in Tableau? Select all that apply.

• Highlighting individual data points
• Providing data to different users based on their particular needs
• Limiting the number of rows or columns in view
• Hiding outliers that do not support the hypothesis

Appropriate uses for filters in Tableau include highlighting individual data points, limiting the number of rows or columns in view, and providing data to different users based on their particular needs.

Question 7

A data analyst creates a dashboard in Tableau to share with stakeholders. They want to save stakeholders time and direct them to the most important data points. To achieve these goals, they can pre-filter the dashboard.

To achieve these goals, they can pre-filter the dashboard. Pre-filtering is useful because it saves time and effort while directing stakeholders to the most important data.

Question 8

An effective slideshow guides your audience through your main communication points. What are some best practices to use when writing text for a slideshow? Select all that apply.

• Choose a font size that audience members can read easily.
• Avoid slang terms.
• Use numerous different text colors and styles.
• Define unfamiliar abbreviations.

Best practices for writing text for a slideshow include choosing a readable font size, avoiding slang terms, and defining unfamiliar abbreviations.

Question 9

You are creating a slideshow for a client presentation. There is a pivot table in a spreadsheet that you want to include. In order for the pivot table to update whenever the spreadsheet source file changes, how should you incorporate it into your slideshow? Select all that apply.

• Insert a PDF of the pivot table
• Embed the pivot table
• Link the pivot table
• Copy and paste the pivot table

In order for the pivot table to update whenever the spreadsheet source file changes, you should link or embed it into the slideshow. This keeps the two files connected, so changes to the spreadsheet will automatically appear in your slideshow.

Week 4: Developing presentations and slideshows

In this part of the course, you’ll discover how to give an effective presentation about your data analysis. You’ll consider all aspects of your analysis when creating the presentation, as well as how to use multiple data sources in the data visualizations you share. In addition, you’ll learn how to anticipate and respond to potential limitations and questions that may arise.

Learning Objectives

• Describe best practices for addressing the question-and-answer section of a presentation
• Consider the caveats and limitations associated with the data in a presentation
• Explain the use of multiple data sources in data visualizations
• Differentiate between strong and weak presentation content
• Describe how junior data analysts are expected use their presentation skills
• Explain principles and practices associated with effective presentations
• Identify appropriate responses to presentation objections

Answers to week 4 quiz questions

L2 Effective presentations

Question 1

Which of the following is an example of a business task? Select all that apply.

• Comparing in-person and online clothing purchasing trends to make stocking decisions
• Theorizing that the amount of coffee purchased per day increases in the summer
• Finding relationships between weather patterns and economic activity
• Identifying a company’s most productive manufacturing plants

Comparing purchasing trends, identifying productive manufacturing plants, and finding relationships between the weather and the economy are examples of business tasks.

Question 2

A supervisor asks a junior data analyst to present two hypotheses regarding a data analytics project. What is the purpose of a hypothesis?

• To describe methods
• To introduce findings
• To theorize about data
• To summarize data

The purpose of a hypothesis is to theorize about your data. Data analysts use them to establish what they want to prove or disprove.

Question 3

Which of the following is an example of an initial hypothesis? Select all that apply.

• A company’s manufacturing plant had lower output in the past month
• A relationship between the holiday season and increased traffic congestion
• An increase in wildlife presence coincides with unusual annual rainfall
• Annual revenue shows a trend that online purchases have increased in the past year

An initial hypothesis is a theory you’re trying to prove or disprove with data. Examples of an initial hypothesis include: annual revenue showing increases in online purchases, a relationship between the holidays and higher traffic, and an increase of wildlife matching increased rainfall.

Question 4

In the McCandless Method, the first step involves communicating to the audience where they should focus and what the graphic is about. Which step is this?

• State the insight of your graphic
• Answer obvious questions before they’re asked
• Calling out data to support your insights
• Introduce the graphic by name

In the McCandless Method, the first step involves communicating to the audience where they should focus and what the graphic is about. This is the step for introducing the graphic by name.

L3 Presentation skills and practices

Question 1

Which techniques can be helpful to prevent nerves before a presentation? Select all that apply.

• Prepare materials beforehand
• Speak quickly so you don’t run out of time
• Describe each graph in-depth
• Channel your excitement

It’s helpful to channel your excitement to keep from getting nervous about a presentation. Preparing materials beforehand can help you remember your material, which may alleviate nerves.

Question 2

Which technique can make it easier to keep your body calm before a presentation?

• Practicing breathing exercises
• Starting with broad ideas
• Applying the five second rule
• Preparing material beforehand

Practicing breathing exercises can make it easier to keep your body calm before a presentation.

Question 3

Which practices are helpful for keeping an audience focused on your presentation? Select all that apply.

• Build in intentional pauses
• Be mindful of nervous habits
• Make eye contact
• Make constant gestures

The practices that help keep an audience focused include making eye contact, reducing nervous habits, and pausing intentionally.

Caveats and limitations to data

Question 1

What is the technique that data analysts use to help them anticipate the questions a stakeholder might have during a Q&A?

• Limitation test
• Practice swing
• Stakeholder brainstorm
• Colleague test

A colleague test is an effective way to anticipate questions a stakeholder might have during a Q&A.

Question 2

You present to your stakeholders, and they express concern about how your results compare to previous results. Which kind of objection are they making?

• Analysis
• Presentation skills
• Data
• Findings

This stakeholder is making an objection about your findings.

Question 3

After your presentation, a stakeholder is concerned about whether your data comes from a reputable source. In what ways should you respond? Select all that apply.

• Acknowledge that the objection is valid
• Follow up with details about the source
• Question why the stakeholder is concerned
• Take steps to investigate the source further

When you receive an objection with merit, the best way to respond is by acknowledging its validity. Then, follow up with details and promise to investigate the matter further.

L5 Listen, respond and include

Question 1

After you finish giving a presentation, and an audience member asks your team about additional information on your topic. Your coworker is answering the question thoroughly, but you notice that the rest of your audience has tuned out. How can you re-engage your audience? Select all that apply.

• Repeat the question
• Redirect to a new question
• Interrupt your coworker
• Ask a question to the audience

If you notice your audience is losing interest, you can redirect to a new question or ask a question to your audience to re-engage them.

Question 2

You answer a question from an audience member, who then seems confused. You conclude that you didn’t understand the question. What should you have done differently to avoid the issue? Select all that apply.

• Repeated the question to clarify
• Elaborated more on the topic
• Listened to the full question
• Provided more context for their answer

In this scenario, two actions could have assisted you in answering the question: listen to the full question or repeat the question to clarify. These strategies can help you answer a question directly and completely.

Question 3

Your audience has several questions after your presentation, and you may not have enough time to answer them all. How should you proceed?

• Involve the whole audience
• Repeat each question
• Keep responses brief and follow up after the presentation
• Understand the context of each question

To answer more questions in less time, keep each response brief and to the point. This way, you answer a question directly and have more time to move onto the next one. After the Q&A, you can follow up with any questions that still need clarification.

Weekly challenge 4

Question 1

A data analyst gives a presentation about predicting upcoming investment opportunities. How does establishing a hypothesis help the audience understand their predictions?

• It visualizes the data clearly and concisely
• It provides context about the presentation’s purpose
• It describes the data thoroughly
• It summarizes the findings succinctly

Establishing a hypothesis provides the audience with context about the analyst’s presentation. In this scenario, it establishes what the analyst wants to prove or disprove about which investment opportunities are most promising.

Question 2

According to the McCandless Method, what is the most effective way to first present a data visualization to an audience?

• Introduce the graphic by name
• Answer obvious questions before they’re asked
• Tell the audience why the graphic matters
• State the insight of the graphic

According to the McCandless Method, the most effective way to introduce a data visualization is to state the name of the graphic.

Question 3

An analyst introduces a graph to their audience to explain an analysis they performed. Which strategy would allow the audience to absorb the data visualizations? Select all that apply.

• Starting with broad ideas
• Practicing breathing exercises
• Using the five-second rule
• Improving body language

When introducing a data visualization, an analyst can use the five-second rule to allow their audience to absorb the data visualizations presented. They can also start with broad ideas to simplify the explanation about the visualization’s purpose.

Question 4

You are preparing for a presentation and want to make sure your nerves don’t distract you from your presentation. Which practices can help you stay focused on an audience? Select all that apply.

• Use short sentences
• Speak as quickly and briefly as possible
• Be mindful of nervous habits
• Keep the pitch of your voice level

Some helpful ways to focus on an audience include being mindful of nervous habits, using short sentences, and speaking with an even pitch. By using these strategies, you can reduce the risk of getting distracted during your presentation.

Question 5

You run a colleague test on your presentation before getting in front of an audience. Your coworker asks a question about a section of your analysis, but addressing their concern would mean adding information you didn’t plan to include. How should you proceed with building your presentation?

• Expand your presentation by including the information
• Remove the section of the analysis that prompted the question
• Keep the concern in mind and anticipate that stakeholders may ask the same question
• Leave the presentation as-is

In this scenario, adding the information can help elaborate on important information. If your colleague has a question about your presentation, it is likely that your audience will too. Addressing concerns brought up during a Colleague Test can help you improve your presentation in ways you might not have anticipated.

Question 6

Your stakeholders are concerned about the source of your data. They are unfamiliar with the organization that ran the analyses you referenced in your presentation. Which kind of objection are they making?

• Data
• Presentation skills
• Analysis
• Findings

When a stakeholder is concerned about the source of your data, they are making an objection about your data. This is when someone objects to the source or relevance of the data you use.

Question 7

A stakeholder objects to the steps of your analysis. What are some appropriate ways to respond to this objection? Select all that apply.

• Explain why you think any discrepancies exist
• Take steps to investigate your analysis question further
• Communicate the assumptions you made in your analysis
• Defend the results of your analysis

When responding to a concerned or objecting stakeholder, you can communicate the assumptions you made to clarify if they are accurate. You can also explain why you think the discrepancies exist and promise to investigate the matter further.

Question 8

You are presenting to a large audience and want to keep everyone engaged during your Q&A. What can you do to ensure your audience doesn’t grow disinterested despite its size?

• Repeat your key findings
• Ask your audience for insights
• Wait longer for the audience to ask questions
• Keep your pitch level

One way to engage a large audience is to ask them if they know anything about the topic you’re presenting about. In a large audience, it is more likely that an audience member may have information or anecdotes to contribute. You can enrich the discussion if they would like to share their insights.

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