Data Visualization

Data visualization is the graphical representation of information and data which helps reveal patterns, trends, gaps, and outliers in the data. Data visualization is a tool that may be taught to students, used to teach, or used to inform teaching practices. 

Teaching Students How to Create Data Visualizations 

  • Convey best practices. Students should: 
    • Identify the goal and audience of their data visualization 
    • Choose the right type of graphic 
    • Avoid misleading visualizations 
  • Introduce students to software relevant to the workforce. For example: 
  • Byrd (2021) describes an activity that used a series of worksheets to introduce data visualization to students with little experience in an undergraduate data visualization course. Since most students have some basic knowledge of Microsoft Excel, it was used for data wrangling and creating basic visualizations in this course before other visualization tools were introduced. The activity led students through acquiring data, examining data, identifying patterns and trends, extracting and representing the data, peer review, improving visualizations, and storytelling. 

Using Data Visualizations in Teaching 

  • If data is used to explain or emphasize a topic being taught, data visualization is an effective way to communicate the data quickly and memorably. 
  • Potential advantages of data visualization (Tableau, n.d.b; Verma, 2023): 
    • Intuitive – many people find visuals easier to understand quickly than just numbers or written explanations. 
    • Simple data sharing – visualization can simplify complex data, making it easier to understand, and can make sharing the data easier. 
    • Better analysis – visualizations can make it easier for students to identify patterns, outliers, and trends in the data their instructor is communicating with them. 
    • Improves retention and engagement – visual data is usually more engaging and easier to remember than raw data. Visualizations can tell a compelling story about what the data represents or implies. 
  • Potential disadvantages of data visualization (Tableau, n.d.b): 
    • Improper visualization – if designed improperly, visualizations may not accurately represent the data. 
    • Incorrect conclusions – if visualizations are unclear or confusing, people may draw different conclusions from the same graphic. 
    • Inexact – visualizations that are not properly labelled may lead viewers to inexact conclusions. 

Using Data Visualization to Inform Teaching Practices 

  • Data visualization is an effective way for instructors to analyze their own data from the courses they teach. 
  • During a course, instructors have access to data from student assessments, such as assignments, quizzes, projects, tests, and exams.  
  • Instructors may also collect data from surveys or polls. The CTE has suggestions for how to collect midterm student feedback which, once collected, can be analyzed using data visualizations. See the CTE Teaching Tip: Collect and use midterm student feedback 
  • Instructors can view statistics and visualizations in LEARN by selecting the ‘View Statistics’ option for any assessment in the gradebook. 

Data Visualization Best Practices (Statistics Canada, 2023; Tableau, n.d.a) 

  • Preparation 
    • Identify the data you have access to. 
    • Identify the goal and audience of your data. 
    • Choose the right type of graph or chart. 
  • Graphic Components 
    • Include a title and labelled axes. 
    • Include clear tick marks on axes so the scale is clear. 
    • Grid lines can improve clarity. 
    • Include text carefully and intentionally, and include a legend, if applicable. 
    • Include citations for the source(s) of the data. 
    • Include colours when necessary to achieve a specific communication objective. 
      • Be mindful of accessibility – viewers of the graphic may be colourblind. 
      • Shades of similar colours look more modern, but can sometimes be difficult to analyze quickly. On the other hand, too many different colours can be confusing as well. 
      • Colours sometimes have preconceived implications. For example, if your data includes temperatures, use red for hot temperatures and blue for cold temperatures. 
  • Aim to keep the visualization simple and predictable. Avoid distracting elements. 
  • Aim to tell a story, avoiding distortions. Data visualizations should be designed to accurately represent the data. 
  • For accessibility, use notes to describe visual elements that are not part of the data table. 

Recommended Tools 

  • Microsoft Power BI – the most popular data visualization software, and it is included in instructors’ Microsoft 365 accounts. 
  • Tableau – popular, user-friendly visualization software used by many companies, particularly in North America. 
  • Microsoft Excel – many students and instructors already have experience using spreadsheets, so creating data visualizations using MS Excel is a natural choice. 
  • Python – one of the most popular programming languages, which has libraries, such as ‘mathplotlib,’ that contain pre-made data visualization functions. 
  • R – a programming language for statistical computing and graphics, which has packages, such as ‘ggplot2,’ that contain pre-made data visualization functions. 
  • Google Charts – a popular, user-friendly online tool that allows users to create a variety of data visualizations which can be easily added to websites. 

References 

Support 

If you would like support applying these tips to your own teaching, CTE staff members are here to help.  View the CTE Support page to find the most relevant staff member to contact. 

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This Creative Commons license lets others remix, tweak, and build upon our work non-commercially, as long as they credit us and indicate if changes were made. Use this citation format: Data Visualization. Centre for Teaching Excellence, University of Waterloo. 

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