> ## Content Index > Fetch the complete content index at: https://fsc.stevens.edu/llms.txt > Use this file to discover other available public pages before exploring further. # FA550 Data Visualization Applications - URL: https://fsc.stevens.edu/fe550-data-visualization-applications/ - Published: 2019-12-02T17:31:05.000Z - Updated: 2026-10-09T20:13:29.000Z - Author: Kajol - Tags: Courses, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14 Course Catalog Description # Introduction Effective visualization of complex data allows for useful insights, more effective communication, and making decisions. This course investigates methods for visualizing financial datasets from a variety of perspectives in order to best identify the right tool for a given task. Students will use a number of tools to refine their data and create visualizations, including: R and associated visualization libraries, Ruby on Rails visualization tools, ManyEyes, HTML5 & CSS 3, D3.js and related javascript libraries, Google Chart Tools, Google Refine, and image-editing programs. | Campus | Fall | Spring | Summer | | ---------- | ---- | ------ | ------ | | On Campus | X | X | | | Web Campus | X | X | | Instructors | Professor | Email | Office | | ------------------------------------------------------------------------------------- | -------------------------- | ---------- | | [Brian Moriarty](https://web.stevens.edu/facultyprofile/?id=1484&ref=fsc.stevens.edu) | 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 There is no textbook, and no software required for this course. Everything the course asks for runs on a free tier or on your own machine. Tableau is available at no cost through Stevens if needed. Weekly readings are optional and are posted in Canvas.> # Additional References TABLEAU LICENSING: - [Download the latest version of Tableau Desktop here:](https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.tableau.com%2Ftft%2Factivation&data=02%7C01%7Cbmoriart%40stevens.edu%7C511a7383206f4a4e2a9408d60853abd3%7C8d1a69ec03b54345ae21dad112f5fb4f%7C0%7C0%7C636705552787053208&sdata=xPysVrwGjjTYQr2a4O8gv2%2BCdnxR5yX6cgTK%2FBbU5TY%3D&reserved=0&ref=fsc.stevens.edu) - Click on the link above and select Get Started. On the form, enter your school email address for Business E-mail and enter the name of your school for Organization. - Activate with your product key: TBD - Already have a copy of Tableau Desktop installed? Update your license in the application: Help menu -> Manage Product Keys Are your students new to Tableau? Share our free [Student Resource Page](https://community.tableau.com/s/students?ref=fsc.stevens.edu) 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 ](https://www.tableau.com/academic/students?ref=fsc.stevens.edu) Additional Online Materials: [Tableau Free Training Videos](https://www.tableau.com/learn/training?ref=fsc.stevens.edu) [Tableau Elearning](https://www.tableau.com/learn/training/elearning?ref=fsc.stevens.edu) [Gephi Tutorials](https://gephi.org/users/?ref=fsc.stevens.edu) [Datawrapper Academy](https://academy.datawrapper.de/?ref=fsc.stevens.edu) [Plotly Chart Studio Tutorials](https://plotly.com/chart-studio-help/tutorials/?ref=fsc.stevens.edu) Grading # Grading Policies - Five assignments(A1 to A5), 10 points each, 50% - AMR Retrospective(pass/fail) 5% - Capstone Proposal 10% - Capstone Check-in 10% - Capstone Final 25% 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 |