Data Visualization in Python with seaborn and Altair Competency (Intermediate Level)

  • 25m
  • 25 questions
The Data Visualization in Python with seaborn and Altair Competency (Intermediate Level) benchmark measures your ability to use seaborn to work with strip and swarm plots, time series data, error bars, logistic and linear regression curves, pair plots, and heatmaps. You will be evaluated on your ability to plot different forms of charts using Altair in order to analyze a variety of datasets and visualize specialized data using a variety of Altair charts. A learner who scores high on this benchmark demonstrates that they have the skills to visualize data with representations of plots, graphs, and charts using seaborn and Altair frameworks

Topics covered

  • access and visualize the top k data points based on a variable from a dataset
  • analyze financial data using candlestick charts
  • apply logistic regressions to categorical data
  • create a map of the United States and plot state-specific information using markers and choropleth maps
  • create a scatter chart where specific points can be selected based on a brush in another linked chart or form element
  • create a scatter plot that is linked to a histogram so that the histogram visualizes all the points in the scatter plot that are selected by a brush
  • create custom figure-level and axis-level strip plots
  • create custom heatmaps to visualize correlation matrices
  • create custom line charts visualizing time series data
  • create various customized area charts such as area charts with multiple categories, streamgraphs, and trellis area charts
  • create various custom scatter plots such as scatter plots where the size of the points represent a variable, scatter plots binned into categories, scatter plots with hollow points, and scatter plots with text labels
  • customize pair plots with KDE curves, regression plots, and contour maps
  • customize various aspects of a chart such as the axis ticks, legend, and title using various functions such as configure_title() and configure_legend()
  • define an Isotype Visualization that represents data by default using dots and can be customized to use emojis to create a pictograph
  • define violin plots such as basic violins, trellis violin plots, and violin plots with color scales
  • enhance bar charts by adding rules representing the mean or median of a distribution, conditional formatting, and creating stacked bar charts
  • generate a variety of box plots such as plain box plots, box plots with categorical color bars, and box plots with continuous color bars
  • generate heat maps to visualize data in the form of a grid
  • perform various customizations on a line chart such as adding a range slider, visualizing multiple variables in a single chart, and adding a new axis for the second column
  • produce Gantt charts to visualize activities, tasks, or events against time
  • recognize the use of area charts as an extension of line charts to sum up all the values in a data set and create an area chart with a gradient in the color of the chart
  • represent data with one dot per value using dot plots and visualize a range of data in a sequence using ranged dot plots
  • use the hue, col, and row input arguments to categorize regression plots
  • visualize individual data points in your dataset using strip plots
  • visualize time series data using figure-level and axis-level line charts