Data Visualization in Python with Seaborn Competency (Intermediate Level)

  • 9m
  • 9 questions
The Data Visualization in Python with Seaborn Competency (Intermediate Level) benchmark will measure your ability to recall, relate, demonstrate, and apply data visualization concepts and techniques in Python using the seaborn library. You will be evaluated on your ability to recognize and apply data visualization concepts, techniques, tools, and functions in Seaborn. A learner who scores high on this benchmark demonstrates that they have the required data visualization skills to understand, apply, and work independently on visualizations in their projects.

Topics covered

  • compare the use cases for swarm plots, bar plots strip plots, and categorical plots
  • configure a FacetGrid to convey more information and to draw one's focus to specific plots
  • create a FacetGrid to visualize distributions within a range of categories
  • define the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating shareable graphs
  • describe what a color palette is and select from the built-in color palettes available
  • identify the kinds of color palettes to use depending on the type of data it will represent
  • recognize what a normal distribution is and what is defined as an outlier
  • use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset
  • work with Seaborn to glean patterns in a dataset by visualizing the relationships between several pairs of variables