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- Multivariate Visualization Methods
- For all but the simplest datasets, complex analytics requires a multivariate understanding of the data being studied. Visual interactivity with the data is a key component of multivariate analytics and makes finding higher dimensional relationships in complex datasets more intuitive. In this module, we’ll take a look at various chart types and visualizations used to express comparisons. You will also have the opportunity to practice correlations in Tableau.
- Statistical Relationships and Distributions
- How important do you think interaction is for general audiences to gain an understanding of complex data sets? In this module, we’ll explore how visual interactivity with the data makes higher dimensional relationships in complex datasets more intuitive and debate whether interactivity in visualization is a hindrance or help for larger audiences.
- Storytelling with Data
- Why does storytelling matter when delivering data to your audiences? What are the principles of storytelling that you should implement into your visuals? In this module, we’ll examine aspects of storytelling and how to structure your story to effectively communicate the right insights to your key stakeholders.
- Ethics, Deception, and Fallacies
- Any time we create a visualization, we create an abstract model of the data. As designers, we must ensure that the data is represented as truthfully and objectively as possible because they have the ability to mislead, deceive, or confuse. In this module, we’ll take a look at some problematic visualizations and how to avoid misrepresenting data. You’ll also put your knowledge to use and perform multivariate visualization methods in Tableau.