02. Visualizations: Overview

This lesson ties everything together by:

Running through types of effective visualizations depending on the problem you are solving, and
Reviewing a data presentation end to end that encompasses all of the concepts we’ve learned thus far.

In the final data presentation example, you’ll see how the key concepts we’ve learned thus far work together. Why does defining a problem statement up front matter so much? Why does building out an issue tree with structured hypothesis help you save time and stay focused? Why does a ghost deck help you think ahead on what your analysis will look and feel like? What are effective visualizations based on varying problem statements? And how does it all look together?

Types of Visualizations:

Relationships:

When your problem statement requires understanding the relationship between 1+features

Example vizualizations include:_ Correlation plots, Regressions_

Comparison:

When your problem statement requires the comparison of two data features or cohorts

Example vizualizations include:_ Box plots_

Temporal:

When your problem statement requires understanding changes to 1+ features across time

Example vizualizations include:_ Time plots_

Distribution:

When enabling your problem statement requires understanding the biases in your data

Example vizualizations include:_ Maps_

Metric Outputs:

When showcasing the results to your problem statement centers on the outcomes (e.g., experimentation, prediction, performance metrics, etc.)

Example vizualizations include:_ Chart metrics_