Course

10156133 Data Visualization
Course Information
Description
Data visualization is figuratively and quite literally the tip, or visible part, of the iceberg. Take this class to get experience creating data visualizations along with the data wrangling and preparation skills required for all good data visualizations. The class is taught with Power BI, but the skills and techniques covered are pertinent when working with any data-viz tool.
Total Credits
3
Course Competencies (Course Outcomes)
  1. Prepare and model data for visualization
    Assessment Strategies
    Individual Project, Skill Demonstration in Lab
    Criteria
    Identify required data elements, sources, and levels of detail
    Extract and organize data using effective tools and methods
    Clean and transform data for analysis and visualization
    Apply dimensional modeling concepts, including basic star schema design, to support reporting and analysis
    Validate data quality, relationships, and summary results for accuracy and consistency

  2. Create effective data visualizations
    Assessment Strategies
    Individual Project, Skill Demonstration in Lab
    Criteria
    Determine the purpose, audience, and analytical question for a visualization
    Select effective visual forms, including charts, tables, and cross-tabular displays, to represent patterns and comparisons accurately
    Construct clear and accurate visualizations using appropriate labels, scales, and layout
    Apply design principles to improve readability and interpretation
    Evaluate and revise visualizations to better communicate findings

  3. Communicate insights from data
    Assessment Strategies
    Individual Project, Skill Demonstration in Lab, Written Product, Oral Presentation
    Criteria
    Analyze visualized data to identify patterns, trends, outliers, and relationships
    Interpret findings in context of the stated question or problem
    Summarize conclusions supported by visual and quantitative evidence
    Summaries include persuasive arguments in favor of data conclusions and influencing stakeholders often from a subordinate position in the organization
    Present findings in a format appropriate for technical and non-technical audiences
    Presentations include data stories, aggregate data, and raw data effectively explained to gain stakeholder trust and validate correctness of any inherent assumptions
    Defend conclusions by explaining methods, assumptions, and limitations