Data Visualization with Python Proficiency (Advanced Level)

  • 30m
  • 20 questions
The Data Visualization with Python Proficiency benchmark will measure your ability to perform data visualizations in Python using advanced plotting and charting techniques, as well as various visualization libraries such as Matplotlib, Plotly, seaborn, and Bokeh. A learner who scores high on this benchmark demonstrates that they can independently work on data visualization in Python.

Topics covered

  • calculate deltas and percentage returns in stock prices
  • change column values by applying functions
  • 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 Bokeh chart and save it in PNG and HTML formats
  • create a FacetGrid to visualize distributions within a range of categories
  • create interactive graphs and image files
  • define the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating shareable graphs
  • define time deltas and date ranges in Pandas
  • describe the basic aesthetic themes and styles available in Seaborn
  • describe what a color palette is and select from the built-in color palettes available
  • display your visualization inline in a Jupyter notebook
  • illustrate how autocorrelation and cross-correlation can be used to identify recurring patterns in data through Matplotlib
  • plot graphs using line and markers
  • plot multiple lines in a single graph using different line styles and markers
  • transform data with user-defined functions
  • use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset
  • use Matplotlib to visualize compositions over a period of time using area charts and changes over time using stem plots
  • visualize proportions in data using donut charts
  • visualize proportions in data using pie charts