what do correlation charts reveal about the data they contain

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:

Course_6_Weekly_Challenge_1

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?

  • Labeling (Correct)
  • Distinguishing
  • Simplifying
  • Subtitling

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

15. Fill in the blank: A data professional includes _____ visualizations in a presentation because stakeholders do not want the visualizations to change unless they choose to edit them.

  • Interactive
  • static (CORRECT)
  • dynamic
  • linked

16. You are a junior data analyst at a web design firm presenting new website features to a client. The client team represents a broad audience of people. What steps should you take to help your visualizations be accessible to everyone? Select all that apply.

  • Minimize contrast between colors
  • Provide text alternatives (CORRECT)
  • Reduce the amount of information in the presentation (CORRECT)
  • Label data directly whenever possible (CORRECT)

17. A data analyst in application engineering visualizes the response times of a web server. The graphic represents how often the data values fall into certain ranges. What type of visualization have they created?

  • Bar graph
  • Pie chart
  • Venn diagram
  • Histogram (CORRECT)

18. A data professional working for a tea shop finds that customers are more likely to purchase hot beverages on cold days. They discover this because two variables in their data rise and fall at the same time. What is this an example of?

  • Tabulation
  • Correlation (CORRECT)
  • Visualization
  • Causation

19. Which of the following statements accurately describe key elements of data visualizations? Select all that apply.

  • Shapes in visualizations should always be three-dimensional. 
  • Colors can be described by their hue, intensity and value. (CORRECT)
  • Value indicates how much light is being reflected. (CORRECT)
  • Lines can be used to add visual form to the data. (CORRECT)

20. While creating a graphic, a data analyst chooses a bright color for a large category and a more muted color for a smaller category. Which element of effective visualization does this describe?

  • Sophisticated use of contrast (CORRECT)
  • Clear meaning
  • Subtitles
  • Refined execution

21. Fill in the blank: Pie charts use _____ to combine the individual parts in a visualization and display them together as a whole, which can help reveal patterns and trends not visible in the original datasets.

  • Sizing
  • patterns
  • data composition (CORRECT)
  • color shading

22. Which of the following statements correctly describe bar graphs? Select all that apply.

  • Bar graphs use one or more lines to display shifts or changes in data over time.
  • The y-axis of a bar graph usually has a scale of values for the variables. (CORRECT)
  • In bar graphs with vertical bars, the x-axis is typically used to represent categories or time periods. (CORRECT)
  • Bar graphs are an effective data visualization when clarifying trends. (CORRECT)

23. Fill in the blank: A data professional includes _____ visualizations in a presentation because stakeholders want the visualizations to reflect the latest data as it becomes available.

  • static
  • dynamic (CORRECT)
  • published
  • embedded

24. You are a junior data analyst at a government agency presenting a new policy to the public. What steps should you take to help your visualizations be accessible to all people, including those with visual impairments? Select all that apply

  • Minimize contrast between colors
  • Reduce the amount of information in the presentation (CORRECT)
  • Label data directly whenever possible (CORRECT)
  • Provide text alternatives (CORRECT)

25. A data analyst in marketing visualizes the distribution of income among different customer segments. The graphic represents how often the data values fall into certain ranges. What type of visualization have they created?

  • Pie chart
  • Venn diagram
  • Bar graph
  • Histogram (CORRECT)

26. Which of the following statements accurately describe key elements of data visualizations? Select all that apply.

  • Lines in visualizations must be thin. 
  • Shapes can be used to add eye-catching contrast to a data story. (CORRECT)
  • The value is how light or dark the colors are. (CORRECT)
  • The intensity of a color is how bright or dull it is. (CORRECT)

27. Fill in the blank: Treemaps are an example of using _____ in visualization, which involves combining the individual parts in a visualization and displaying them together as a whole. 

  • patterns
  • color shading
  • data composition (CORRECT)
  • sizing

28. Which of the following statements correctly describe bar graphs? Select all that apply.

  • Bar graphs use segments to represent the proportions of each data category compared to the whole.
  • The horizontal line of a bar graph, usually placed at bottom, is called the x-axis. (CORRECT)
  • Bar graphs use size contrast to compare two or more values. (CORRECT)
  • The vertical line of a bar graph usually placed to the left is called the y-axis. (CORRECT)

29. Fill in the blank: A data professional includes _____ visualizations in a presentation because stakeholders want to monitor data streams for information that is constantly changing.

  • published
  • embedded
  • dynamic (CORRECT)
  • static

30. You are a junior data analyst presenting quarterly earnings to shareholders, including people who have visual and hearing impairments. What steps should you take to help your visualizations be accessible to everyone? Select all that apply.

  • Minimize contrast between colors
  • Label data directly whenever possible (CORRECT)
  • Provide text alternatives (CORRECT)
  • Reduce the amount of information in the presentation (CORRECT)

31. A data professional working in online sales finds that products frequently discussed on social media tend to sell more units. They discover this because two variables in their data rise and fall at the same time. What is this an example of?

  • Tabulation
  • Causation
  • Correlation (CORRECT)
  • Visualization

32. While creating a data visualization, a data analyst chooses a warm color for positive data and a cool color for negative data. Which element of effective visualization does this describe?

  • Subtitles
  • Clear meaning
  • Refined execution
  • Sophisticated use of contrast (CORRECT)

33. Fill in the blank: Donut charts use _____ to combine the individual parts in a visualization and display them together as a whole. 

  • patterns
  • sizing
  • color shading
  • data composition (CORRECT)

34. A data analyst in the science field visualizes the distribution of the length of a species of fish. The graphic represents how often the data values fall into certain ranges. What type of visualization have they created?

  • Pie chart
  • Histogram (CORRECT)
  • Bar graph
  • Venn diagram

35. While creating a chart, a data analyst chooses a darker shade for outliers and a lighter tint for data points that are above or below a certain threshold. Which element of effective visualization does this describe?

  • Clear meaning
  • Sophisticated use of contrast (CORRECT)
  • Subtitles
  • Refined execution

36. A data professional working for a manufacturing company finds that products with certain temperature variations are more likely to have defects. They discover this because two variables in their data rise and fall at the same time. What is this an example of?

  • Correlation (CORRECT)
  • Causation
  • Visualization
  • Tabulation

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.