Course 6 – Share Data Through the Art of Visualization Quiz Answers
Week 1: Visualizing Data
GOOGLE DATA ANALYTICS PROFESSIONAL CERTIFICATION
Complete Study Guide
Visualizing Data introduction
Visualizing data is an essential part of the Google Data Analytics Certification available through Coursera. In this part of the course, you’ll learn how to create effective visualizations for your data analysis and what do correlation charts reveal about the data they contain. This includes exploring various approaches to presenting information, such as accessibility and design thinking, as well as factors that impact the success of a visualization.
Visualizing data can help make it easier for people to understand what’s going on in your analysis and can be used to communicate insights more effectively. With the right approach and tools, anyone can create visually compelling displays of their data. By completing this certification program, you’ll acquire valuable skills that will prepare you for a career as a data analyst.
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
Test your knowledge on data visualization
1. Fill in the blank: Correlation charts show _____ among data.
- Relationships (Correct)
- causation
- outcomes
- changes
Correct: Correlation charts show relationships among data.
2. When does causation occur?
- When an action directly leads to an outcome (Correct)
- When an action possibly leads to an outcome
- When an action potentially leads to different outcomes
- When multiple actions lead to the same outcome
Correct: Causation occurs when an action directly leads to an outcome. Causation indicates a clear cause and effect.
3. Which of the following are part of McCandless’s elements of effective data visualization? Select all that apply.
- The moral
- The structure
- The goal (Correct)
- The visual form (Correct)
Correct: There are four elements of effective data visualization according to David McCandless. These include the information, the story, the goal, and the visual form.
test your knowledge on designing data visualization
1. Which element of design can add visual form to your data and help build the structure for your visualization?
- Space
- Shape
- Line (Correct)
- Movement
Correct: Lines add visual form to your data and help build the structure for your visualization.
2. Which of the following are elements for effective visuals? Select all that apply.
- Clear goal
- Refined execution (Correct)
- Clear meaning (Correct)
- Sophisticated use of contrast (Correct)
Correct: The elements for effective visuals are clear meaning, sophisticated use of contrast, and refined execution.
3. Fill in the blank: Design thinking is a process used to solve complex problems in a _____ way.
- step-by-step
- user-centric (Correct)
- action-oriented
- pre-attentive
Correct: 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.
4. While creating a data visualization for your stakeholders, you realize certain colors might make it more difficult for your audience to understand the data. So, you choose colors that are more accessible. What phase of the design process does this represent?
- Define
- Prototype
- Empathize (Correct)
- Test
Correct: 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.
hands-on activity: making your own visualization
1. Which of the following are necessary to consider while making an effective visualization? Select all that apply.
- The design thinking process (Correct)
- The brand of visualization software you use
- The needs of your audience (Correct)
- The type of data you are visualizing (Correct)
Correct: In order to make an effective visualization, you must consider the type of data you’re visualizing, the needs of your audience, and the design thinking process. An effective visualization can be made in any visualization software. Going forward, you can use your knowledge of creating data visualizations in the chart editor to explore more types of data visualizations. This will help you better present your data and findings to peers and stakeholders.
Test you knowledge on exploring data visualization
1. What are the three basic visualization considerations? Select all that apply.
- Labels (Correct)
- Subtitles (Correct)
- Text
- Headlines (Correct)
Correct: The three basic visualization considerations are headlines, subtitles, and labels.
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.
- True (Correct)
- False
Correct: Directly labeling a data visualization helps viewers identify data quickly. Legends are often less effective because they are positioned away from the data.
3. Why do data analysts use alternative text to make their data visualizations more accessible?
- To make data visualizations easier to read
- To add context to the data visualization
- To make the presentation of data clearer
- To provide a textual alternative to non-text content (Correct)
Correct: 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.
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.
- Focus on necessary information over long chunks of text (Correct)
- Simplify your visualization (Correct)
- Do not include labels
- Avoid overly complicated charts (Correct)
Correct: 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.
GOOGLE DATA ANALYTICS COURSERA ANSWERS AND STUDY GUIDE
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Share Data Through the Art of Visualization Weekly Challenge 1
1. A data analyst working for an e-commerce website creates the following data visualization to show the amount of time users spend on the site:
What type of visualization is it?
- Correlation chart
- Line graph
- Scatter plot
- Histogram (Correct)
Correct: It is a histogram. Histograms demonstrate how often data values fall into certain ranges.
2. What do correlation charts reveal about the data they contain?
- Relationships (Correct)
- Changes
- Visualization
- Causation
Correct: Correlation charts indicate relationships among data.
3. You are creating a presentation for stakeholders and are choosing whether to include static or dynamic visualizations. Describe the difference between static and dynamic visualizations.
- Static visualizations separate out the individual elements of a single visualization. Dynamic visualizations combine multiple visualizations into a whole.
- Static visualizations combine multiple visualizations into a whole. Dynamic visualizations separate out the individual elements of a single visualization.
- Static visualizations are interactive and can automatically change over time. Dynamic visualizations do not change over time unless they’re edited.
- Static visualizations do not change over time unless they’re edited. Dynamic visualizations are interactive and can automatically change over time. (Correct)
Correct: Static visualizations do not change over time unless they’re edited. Dynamic visualizations are interactive and can automatically change over time.
4. What are the key elements of effective visualizations you should focus on when creating data visualizations? Select all that apply.
- Visual form
- Sophisticated use of contrast (Correct)
- Refined execution (Correct)
- Clear meaning (Correct)
Correct: The elements for effective visualization are clear meaning, sophisticated use of contrast, and refined execution.
5. Fill in the blank: Design thinking is a process used to solve problems in a _____ way.
- analytical
- critical
- user-centric (Correct)
- design-centric
Correct: Design thinking is a process used to solve complex problems in a user-centric way.
6. Fill in the blank: During the _____ phase of the design process, you start to generate data visualization ideas.
- test
- ideate (Correct)
- empathize
- define
Correct: There are five phases of the design process: empathize, define, ideate, prototype, and test. During the ideate phase of the design process, you start to generate data visualization ideas.
7. Fill in the blank: A data analyst can make their visualizations more accessible by adding _____, which are text explanations placed directly on the visualizations.
- callouts
- legends
- labels (Correct)
- subheadings
Correct: A data analyst can make their visualizations more accessible by adding labels, which are text explanations placed directly on the visualizations. Labeling data directly instead of relying on legends can make data visualizations more accessible.
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 is a method data analysts use to distinguish elements?
- Separate the foreground and background (Correct)
- Ensure all elements are highlighted equally
- Add a legend
- Use contrasting colors and shapes
Correct: Data Analyst distinguish elements of data visualization by separating the foreground and the background and using contrasting colors and shapes.
9. While creating a chart to share their findings, a data analyst uses the color red to make important data stand out and separate it from the rest of the visualization. Which element of effective visualization does this describe?
- Sophisticated use of contrast (Correct)
- Clear meaning
- Refined execution
- Subtitles
A data analyst using color to make important data stand out and separate it from the rest of the visualization is an example of sophisticated use of contrast. Sophisticated use of contrast helps separate the most important data from the rest using visual context.
10. 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?
- Histogram (Correct)
- Line Graph
- Scatter Plot
- Correlation Chart
Correct: To demonstrate how often data values fall into certain ranges, the data analyst should use a histogram
11. 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?
- Correlation (Correct)
- Visualization
- Causation
- Tabulation
Correct: 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.
12. 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.
- Dynamic (Correct)
- Geometric
- Aesthetic
- Static
Correct: 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.
13. A data analyst makes sure that they approach problems in a user-centric way. What element of data analytics does this describe?
- Design Thinking (Correct)
- Critical Thinking
- Analytical Thinking
- Structure Thinking
Correct. Design thinking is a process used to solve complex problems in a user-centric way.
14. A data analyst wants to make their visualizations more accessible by adding text explanations directly on the visualization. What is this called?
- Labelling (Correct)
- Distinguishing
- Simplifying
- Subtitling
Correct. This is labeling. Labeling data directly instead of relying on legends can make data visualizations more accessible.
Visualizing Data CONCLUSION
In conclusion, visualizing data is an essential part of the Google Data Analytics Certification available through Coursera. In this part of the course, you’ll learn how to create effective visualizations for your data analysis. This includes exploring various approaches to presenting information, such as accessibility and design thinking, as well as factors that impact the success of a visualization.
Visualizing data can help make it easier for people to understand what’s going on in your analysis and can be used to communicate insights more effectively. Join the learning experience in Coursera today and see how you can use visualization to enhance your own data analytics.
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