Course Summary
Credit Type:
ACE Course Number:
29 hours and 16 lab hours
Dates Offered:
Credit Recommendation & Competencies
Level Credits (SH) Subject
Upper-Division Baccalaureate 3 data visualization


The course objective is to explore the significance of creating user-centered visuals and best practices to follow, along with various types of charts, plots, graphs and diagrams to represent data visually. The course will cover using Excel and BI tools, such as QlikView, to create eye-catching visualizations from various imported data formats. It will also explore creating interactive dashboards and infographics for visualization projects. Finally, discover techniques for creating visualizations using various Python libraries like Matplotlib, Plotly, and Bokeh before modeling the data.

Learning Outcomes:

  • explore data visualization best practices
  • visualize data in Excel
  • visualize data using QlikView
  • create infographics using Infogram
  • create infographics using Visme
  • visualize data with Matplotlib
  • visualize data with Bokeh and Plotly.

General Course Topics:

  • Data visualization: best practices for creating visuals
  • Excel visualization: getting started with Excel for data visualization
  • Excel visualization: building column charts, bar charts, and histograms
  • Excel visualization: visualizing data using line charts and area charts
  • Excel visualization: plotting stock charts, radar charts, treemaps, and donuts
  • Excel visualization: building box plots, sunburst plots, Gantt charts, and more QlikView: getting started with QlikView for data visualization
  • QlikView: creating line charts, combo charts, pivot tables, and block charts
  • QlikView: creating Mekko charts, radar charts, gauge charts, and scatter charts
  • Data visualization with Excel and BI tools
  • Infogram: getting started
  • Infogram: advanced features
  • Visme: introduction
  • Visme: exploring charts Visme: designing a presentation
  • Python and Matplotlib: getting started with Matplotlib for data visualization
  • Python and Matplotlib: creating box plots, scatter plots, heatmaps, and pie charts
  • Data visualization: building interactive visualizations with Bokeh
  • Data visualization: more specialized visualizations in Bokeh
  • Data visualization: getting started with Plotly
  • Data visualization: visualizing data using advanced charts in Plotly creating infographics and data visualization with Python
Instruction & Assessment

Instructional Strategies:

  • Computer Based Training
  • Laboratory
  • Practical Exercises

Methods of Assessment:

  • Examinations
  • Quizzes

Minimum Passing Score:

Supplemental Materials