Data Visualization Best Practices
- Visualization Fundamentals
- Visualization is a crucial skill for data analysts across all disciplines. Viewing data graphically often provides greater intuition than by using statistics or mathematics alone. In this module, we’ll explore the fundamentals of visualization and discuss how visualizations can achieve better insight into data as well as effectively communicate results and conclusions.
- Design Principles for Effective Visualizations
- To create effective visuals, analysts must understand and be able to explain why specific graphical elements must be included, eliminated, or modified. In this module, we’ll investigate why certain questions are best answered by specific visual cue patterns, discuss which psychological perception theories should be considered during the construction of data visualizations, and cover the universal framework for representing visual data, the grammar of graphics.
- Univariate Visualization Methods
- Univariate visualization methods apply the grammar of graphics to the representation of a dataset’s fundamental properties and structures in terms of single variable mappings to visual encodings. In this module, we’ll explore specifics for how to view the data and what to visualize and discuss what mapping of data is best suited for highlighting and extracting insights from the data.
- Standard Univariate Visualizations
- Instead of reinventing the wheel with every visualization, analysts use several common chart types so there is no ambiguity about the decoding and interpretation of the data. In this module, we'll explore some standard tools and techniques that are used to prepare a fully crafted set of visualizations for the purpose of storytelling. Before completing this module, you will use Tableau to conduct a univariate analysis to sample data, compare dimensions vs. measures, and practice linking visualizations.