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Cross-listed with DTSA 5304

  • Course Type: Elective
  • Specialization: Fundamentals of Data Visualization
  • Instructor: Dr. Danielle Szafir, Visiting Assistant Professor, ATLAS Institute, University of Colorado Boulder; Assistant Professor, Computer Science, University of North Carolina Chapel Hill
  • Prior knowledge needed: Python, foundation in Data Science

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Learning Outcomes

  • Develop a toolkit for exploring and communicating complex data using visualization

  • Produce basic data visualizations using a chosen dataset

  • Compare methods for visualizing data and understand how these methods may guide users towards different conclusions

  • Evaluate how effectively a visualization conveys target data

Course Content

Duration: 7 hours

In this module, you will learn the foundations of visualization design. You will walk through the key components of a visualization, how we effectively represent data using channels like color, size, and position, and some ground rules for honest and effective visualization. You will also gain preliminary exposure to Altair, a Python library for rapidly generating interactive visualizations. Each week will also include either two readings or one reading and one notebook activity.

Duration: 3 hours

In this module, you will learn how to choose the right visualization for a given scenario. You will learn how to reason about the different kinds of questions people ask with visualization and, how to align your design with that task. The module will cover basics of task analysis, methods for task elicitation, and foundational knowledge of visual perception for design. Each week will also include two external readings or one reading and one notebook activity.

Duration: 3 hours

In this module, you will learn how to assess the effectiveness of your visualization. You will learn both qualitative and quantitative approaches for evaluating visualizations as well as how to isolate key elements for assessment and iteration. The module will cover basics of insight-based evaluation, interview studies, and experimental design and analysis. Each week will also include two external readings or one reading and one notebook activity.

Duration: 5 hours

Throughout the Modules, you have found a dataset, characterized the corresponding goals and tasks you want to conduct with that data, designed preliminary approaches, and outlined how you would evaluate those approaches. For your final project, you will put these ideas into practice by executing on the project plan outlined in your prior posts.

Notes

  • Cross-listed Courses: Courses that are offered under two or more programs. Considered equivalent when evaluating progress toward degree requirements. You may not earn credit for more than one version of a cross-listed course.
  • Page Updates: This page is periodically updated. Course information on the Coursera platform supersedes the information on this page. Click the View on Coursera button above for the most up-to-date information.