Data Visualization Literacy

  • 30m
  • 20 questions
The Data Visualization Literacy benchmark will measure your ability to recall and relate the underlying data visualization concepts in Excel. You will be evaluated on your ability to recognize the foundational concepts of data visualization, its uses, and best practices like the basics of visualization in Excel, Qlikview, and Python. A learner who scores high on this benchmark demonstrates that they have the basic data visualization skills to understand and grasp the visualization techniques and their uses.

Topics covered

  • add up/down bars and high/low lines to a line chart, copy and paste a line chart to a new sheet, and format the line chart
  • Compare categorical data by category against continuous values using bar charts
  • create a histogram to visualize the frequency counts of data in bins using bars
  • create various basic line charts visualizing random data using Matplotlib and pyplot
  • Create various special histograms, such as a histogram visualizing multiple columns
  • customize various aspects of a line chart, such as the color of the line
  • identify use cases that require tables and bar charts
  • illustrate how autocorrelation and cross-correlation can be used to identify recurring patterns in data through Matplotlib
  • import data from a CSV file using pandas and visualize it with a basic line chart
  • import data from Excel into QlikView
  • list use cases and best practices for line charts and pie charts
  • open up Microsoft Excel and read in data from an Excel workbook and a CSV file
  • outline different presentation types and considerations before choosing a chart type for your data
  • recognize the importance of data visualization in the age of big data
  • recognize use cases that require histograms and box plots
  • use Matplotlib to create a heatmap that visualizes correlations and has labels for each correlation
  • use Matplotlib to create box-and-whisker plots to display various statistics, such as the median, upper and lower quartiles and outliers
  • use Matplotlib to use correlation heatmaps to visually represent covariate relationships
  • use Matplotlib to visualize the relationship between two continuous variables using scatter plots
  • visualize data using built-in Excel charts, explore various column chart types, and create basic and clustered column charts