Data Visualization Proficiency (Advanced Level)

  • 30m
  • 20 questions
The Data Visualization Proficiency benchmark will measure your ability to recall, relate, demonstrate, and apply the data visualization concepts and techniques in Excel, QlikView, and various Python visualization libraries. You will be evaluated on your ability to recognize and apply the concepts of data visualization techniques, tools, and functions in Excel, Qlikview, Infographics, and Python. A learner who scores high on this benchmark demonstrates that they have the required data visualization skills to understand, apply, and work independently on the visualizations in their projects.

Topics covered

  • configure elements such as text and icons to create an infographic
  • create a box-and-whisker-plot with separate categories on the x-axis and based on the color of the box, then add lines linking the means for all categories
  • create a Sankey diagram to visualize data
  • create a waffle or KPI chart with a grid of percentages that update based on an index that in turn updates based on the value of a spin button
  • create a waterfall chart to show the cumulative effect of positive and negative values over a specified period of time to add up to a total final value
  • create a waterfall chart to visualize the cumulative effect of positive and negative values when analyzing the financials of a company
  • create bar and lollipop charts that visualize multiple related variables in one chart
  • create drawn Lollipop charts to compare categorical data to continuous values
  • create maps to visualize geographical data
  • customize scatter plots to have three dimensions using the sizes of data points
  • display the relationship between two variables and understand what kind of model might be most useful to represent that relationship
  • identify relationships between entities using a network chart
  • represent data using a clustered bar chart
  • use Matplotlib to create a heatmap that visualizes correlations and has labels for each correlation
  • use Matplotlib to create exploded pie charts and treemaps
  • use Matplotlib to visualize compositions over a period of time using area charts and changes over time using stem plots
  • visualize data using a stacked bar chart
  • visualize proportions in data using donut charts
  • visualize the flow of data using a Sankey diagram
  • work with different kinds of gauge charts, such as tanks, thermometers, and joysticks