FAQ: Misleading and Confusing Graphs - Conclusion

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This community-built FAQ covers the “Conclusion” exercise from the lesson “Misleading and Confusing Graphs”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Machine Learning/AI Engineering Foundations
Data Scientist: Analytics Specialist
Data Scientist: Natural Language Processing Specialist
Data Science Foundations
Data Scientist: Inference Specialist
Data Scientist: Machine Learning Specialist

Principles of Data Literacy

FAQs on the exercise Conclusion

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Its great to know these are established best practices that are being taught here. In reality, how many people would be responsible typically for a visualization on a work project? Can this vary? Is it one person or a team effort? Is it different in a larger/smaller project or company?

Edit (spoiler alert): I disagreed with some of the reasoning the answers gave on the project for this section. I felt the yellow background, especially that particular yellow was awful as a background. It makes the visualization seem old and of poorer taste. On #3, I felt the type of the title was a more appropriate size, especially when you consider some, if not many, viewers have less than 20-20 vision and may have to squint to read the title of #1 and #2. I am also not opposed to having a “busy” background so long as it is not too busy. It should be minimal. It depends on the culture of the organization, and if the data would be about them (if it were another example). It can be argued that data viz is a form of art, and with art there are varying degrees of what people find appropriate and aesthetically pleasing, and you can’t please them all so finding a medium when there is room to do so, I think would be the safest bet for considerations described, while keeping best practices in place.