FA550 Data Visualization Applications
Course Catalog Description
Introduction
| Campus | Fall | Spring | Summer |
|---|---|---|---|
| On Campus | X | X | |
| Web Campus | X | X |
Instructors
| Professor | Office | |
|---|---|---|
| Brian Moriarty | brian.moriarty@stevens.edu | Babbio 629 |
More Information
Course Outcome
- Use AI tools to generate, critique and refine data visualizations, and judge where a given tool is likely to be wrong from evidence the student gathered rather than from the tool's reputation.
- Select visualization types and apply visual encoding principles to produce accurate and accessible presentations, and defend each choice against the alternative that was rejected.
- Design and implement interactive dashboards whose coordination supports a named reader answering a named question.
- Profile, clean and transform data into analysis-ready form, documented so that another person can reproduce the result from the committed artifacts.
- Construct data narratives with a claim specific enough to be wrong, supported by the evidence shown, and adapted to a named audience.
- Account for AI use in their own work: mark every consequential interaction Accept, Modify or Reject with reasoning specific to the data, and name the limits of what their work can show.
- Scope and deliver an end-to-end visualization project, from a falsifiable question through a defensible artifact, and defend its decisions under questioning.
Course Resources
Textbook
Additional References
TABLEAU LICENSING:
Are your students new to Tableau? Share our free Student Resource Page to help them get started. Students can continue using Tableau after the class is over by individually requesting their own one-year license through the Tableau for Students
Additional Online Materials:
Tableau Free Training Videos
Tableau Elearning
Gephi Tutorials
Datawrapper Academy
Plotly Chart Studio Tutorials
Grading
Grading Policies
Lecture Outline
| Week or Module | Topic | Associated Reading | Assignments |
|---|---|---|---|
| Week 01 | Introduction | Optional, in Canvas | |
| Week 02 | Research Methods and Data Discovery | Optional, in Canvas | |
| Week 03 | Visualization Types and Chart Selection | Optional, in Canvas | A1, Tool Calibration |
| Week 04 | Critique and Redesign | Optional, in Canvas | |
| Week 05 | Data Preparation and Pipeline Thinking | Optional, in Canvas | A2, Critique and Redesign |
| Week 06 | Dataset Selection and Dashboard Foundations | Optional, in Canvas | Dataset gate |
| Week 07 | Prompts the Elicit Analytical Reasoning | Optional, in Canvas | A3, The Pipeline |
| Week 08 | Statistical Analysis and Advanced Analytics | Optional, in Canvas | |
| Week 09 | Advanced Dashboard Development | Optional, in Canvas | A4, The Analysis |
| Week 10 | Data Storytelling and Narrative Design | Optional, in Canvas | A5, The Story |
| Week 11 | Capstone Project Launch | Optional, in Canvas | AMR Retrospective; Capstone Proposal |
| Week 12 | Performance and Cloud Deployment | Optional, in Canvas | |
| Week 13 | Capstone Check-in and Ethics Review | Optional, in Canvas | |
| Week 14 | MCP and Tool Integrations | Optional, in Canvas | Capstone Check-in |
| Week 15 | Capstone presentations | Capstone Final |
This table shows semesters in which this course has typically been offered.
To create your study plan, please confirm future offerings with your academic advisor or the course instructor.
If you are interested in taking this course in an upcoming semester that is not listed, please fill out this form.