Data Visualization Competency

  • 30m
  • 20 questions
The Data Visualization mastery Competency benchmark will measure your ability to recall, relate, and demonstrate applying the underlying Data Visualization concepts and techniques in Excel, Qlikview and Python. You will be evaluated on your ability to recognize the concepts of data visualization, techniques, 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 and apply visualization techniques in their projects.

Topics covered

  • classify the advantages and disadvantages of variants of line charts, such as stacked line charts, 100% stacked line charts, and 3D line charts
  • configure a basic bar chart and table that influence each other
  • create a basic area chart, illustrate a major disadvantage of an ordinary area chart, then change the opacity of areas in order to overcome this problem
  • create a histogram to bin a continuous data series, visualize the counts of rows in each bin, and create histograms for normally and non-normally distributed data series
  • create and customize basic grid charts
  • create basic combo charts with bars and lines
  • customize a clustered column chart, add trendlines and format the chart, then create a basic line chart
  • customize histograms using various options, such as the overflow and underflow bins to truncate the histogram on either side
  • describe the difference between a column chart and a bar chart, create a clustered bar chart, and perform various operations, such as sorting on that chart
  • display your visualization inline in a Jupyter notebook
  • identify the components of a Plotly graph
  • illustrate the use of the gradient fill feature, change various aspects of a gradient-filled line chart, and work with radial gradients and linear gradients
  • import an Excel file as an XML file and separately import a JSON file into Excel and open it up in the Power Query Editor
  • use a pivot table to access aggregate values in hierarchical data
  • use a radar chart to visualize the multivariate ordinal data and illustrate the advantages and disadvantages of radar charts
  • use Matplotlib to create exploded pie charts and treemaps
  • use the pie-of-pie chart type to create one pie chart with an 'other' category and a second pie chart that expands the fields in that 'other' category
  • use trendlines to explore the suitability of various kinds of regression models, such as linear regression and polynomial regression via a line chart
  • visualize statistical data using box-and-whisker plots
  • visualize the trend of a stock's performance based on high, low, and close values over a specific period of time using the Excel High-Low-Close stock chart