Week 1: Visualizing Data
Visualizing Data introduction
This Google Data Analytics Certification from Coursera allows you to visualize data. In this segment of the course, there are sample visualizations that advocate effective data analysis and provide insights into correlation charts that reveal useful information about data. Several approaches to making information available were explored, with emphasis on accessibility considerations, design thinking, and other factors that make a visualization successful.
This makes complex analysis understandable and can enable communicating insights more effectively. With appropriate techniques and tools, anyone can create a compelling visualization of data-an informative one, indeed. This certification will give you a valuable skill set to prepare you for the field of data analyst.
Objectives for Learning:
- Understand design thinking as it pertains to data visualization.
- Recognize how data visualizations represent the meaning of the data itself and, thus, the processes of data analysis.
- Identify accessibility challenges posed by the specific technique of data visualization.
- Acknowledge the importance of data visualization for data analysts.
- Learn basics of 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 exhibit the association between different named data sets.
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 a direct action causes a specific outcome, creating a cause-effect link in that instance.
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: According to David McCandless, there are four main things in a good data visualization: the information, the story, the goal, and the means of visualizing.
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 give a visual structure to your data and help contribute to the whole framework of 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 salient elements of successful visuals are their lucid meaning, a deftly applied contrast, and a clean 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 user-centric approach to problem-solving that encourages data analysts to explore various ways of crafting successful 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: Selecting appropriate colors for the visualization is part of the empathize phase in design thinking. This is the same phase during which the target audience is thought to invoke feelings and their respective needs to ensure that the visual data communicated does resonate well.
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: For effective visualization, you must focus on understanding the type of data you want to visualize, audience needs, and the design thinking process. You can create very effective visualizations even with any visualization software. Using your skills in creating data visualizations in the chart editor will get you exploring many more types of visualization. This would, in turn, enable the presentation of data and findings more satisfactorily 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: Data visualization labeling is direct, identifying the piece of information, and making it more accessible, whereas legending it is less effective because it is usually moved away from the data and requires effort from viewership to make the connection.
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 describes textually the non-text elements of the visualization. This gives users who need alternate means of retrieving data (for example through a screen reader) access to the interpretation of the display.
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: Focus Simplifying data visualisations for your audience. Not a messy visual presentation to exclude unnecessary detail that could take attention from the main message.
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: A histogram is a kind of chart which indicates how often data values fall within certain intervals or ranges. It helps visualize the distribution of data.
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 are permanent and don’t change unless edited manually, whereas dynamic visualizations can be manipulated and, as the term implies, automatically refresh over time, allowing for a more fluid and engaging experience.
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: Effective visualization includes sec clear meaning, subtle contrast use, and fine 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 way of solving problems in a human-centric manner to develop innovative solutions to the most complex of challenges, with a strong focus on user need and understanding.
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: The five phases of the design process are empathy, define, ideation, prototype, and test. During the ideation stage, you’re generating ideas for visualizing your data: brainstorming ways to properly convey the information.
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: To a data analyst, adding labels will increase the accessibility of the visualization because it gives the explanation directly on the visuals. This makes data easier to interpret, that is, the viewers do not have to look for legends that are in other parts of the visualization.
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 analysts differentiate elements in a data visualization through separation of the form of the visualization into its foreground and background, contrasting colors, and shapes. This is to help highlight important points of the data and improve the overall understanding of the visualization.
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
For example, a very sophisticated use of contrast is when a data analyst highlights certain important data against the rest of the data by color. It helps in focusing attention on the major data and provides visual guidance for interpretation.
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: A histogram is a type of plot that allows a data analyst to show how often data values fall into particular intervals. It uses these intervals to present the distribution of data and make it easier to visualize regularities and trends by grouping the values in different intervals.
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 two variables going up and down simultaneously, this is an example of correlation. Based on the definition, correlation means the extent at which two different variables change in relation with each other; it indicates if yes or no they are connected with how strongly they are conductive.
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: It is when two variables rise and fall simultaneously, which has been observed by a data analyst, that correlation is true. Correlation refers to a measure of the extent to which two variables change with each other, indicative of what could be a relationship existing between them.
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. Labeling is. Data visualizations become more accessible and easier for viewers to understand by directly labeling data on the visualization instead of legends because viewers can simply identify the labels with the relevant data points.
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 summary, data visualization is one of the many important contents in Google Data Analytics Certification offered through Coursera. This module entails learning how to practice impactful visualization methods within data analysis. It includes utilizing various approaches to data delivery, from accessibility to applying design thinking and the conditions for a successful visualization.
Data visualization simplifies complex analysis and facilitates better communication, which makes understanding simpler. Join the Coursera learning experience today and find out how visualizations can improve data analytics skills for your own good.