FE550 Data Visualization Applications

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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: Tableau 9.3/10 Beta, R and associated visualization libraries, HTML5 & CSS 3, D3.js and related javascript libraries, Open Refine, basic Python scripting, and image-editing programs.
Campus Fall Spring Summer
On Campus X X
Web Campus X X

Instructors

Professor Email Office
Brian Moriarty
brian.moriarty@stevens.edu



More Information

Course Description

Tools Used In This Course
  • Visualization Ecosystem
  • Additional Resources
  • A more concise, but somewhat different perspective

Course Outcomes

* Develop knowledge of tools for visualizing datasets with emphasis on financial datasets.
  • Develop a programmatic understanding of translating data into useful visual forms
  • Develop a critical vocabulary to engage and discuss information visualization
  • Develop an understanding of data visualization theory.
  • Understanding of ethical considerations for data visualization


Course Resources

Textbook

* Miller, James D. Big Data Visualization, Packt Publishing, 2017. ISBN:​ 978-1785281945
  • Milligan, Joshua N.​ Learning Tableau 10, 2nd edition. Packt Publishing, 2016. ISBN: 978-1-78646-635-8
  • Tufte, Edward. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press, 2001. Print. ISBN: 978-0961392147
  • Gohil, Atmajitsinh. ​ R Data Visualization Cookbook. Packt Publishing, 2015. Print. ISBN: 978-1-78398-950-8

Additional References

There are many solid texts available through Safari Books Online including, but certainly not limited to:
  • Interactive Data Visualization for the Web, by Scott Murray; ISBN: 978-1-4493-3973-9
  • Data Visualization with d3.js, by Swizec Teller; ISBN: 978-1-78216-000-7
  • Data Visualization: A Successful Design Process, by Andy Kirk; ISBN: 978-1-84969-346-2
  • The Functional Art, by Albert Cairo; ISBN: 978-0-13-304118-7

It is likely more valuable in this course to be concerned with current trends in data visualization. Some good resources include:

TABLEAU LICENSING:

  • Download the latest version of Tableau Desktop here:
  • 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: TC78-BBBA-90C0-C3B8-92DB
  • 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 ​Data Analytics for University Students guide 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 program here Need help? Find answers to frequently asked questions​ here​.

TABLEAU COURSE MATERIALS:

All Tableau course materials are available in the shared Files directory for this course.

Data Sets:

Through the Hanlon Financial Systems Lab, each student enrolled in this course has free access to various historical data sets. See the HFSL Wiki: http://web.stevens.edu/hfslwiki/index.php?title=Main_Page​ for more details. Other data sets may be found from various locations across the web, though you may find that the data will need to be “cleaned” before usage in visualization projects and assignments. These sources include, but are not limited to:



Grading

Grading Policies

Course grades are calculated precisely based on the following components: 1) Online Discussion: 20% 2) Team Data Product Submission: 30% (5% for proposal, 10% for update, 15% for final submission) 3) Assignments: 25% (5-5-5-10% for assignment 1-2-3-4) 4) Individual Final Project (broken down by submission component): 25% (5% proposal, 5% for update, 15% for final submission)

Participation grade will be assigned based on active participation in weekly discussions and completion of assigned peer reviews based on readings, exercises, and additional contributions. You will serve yourself well by completing course readings and/or finding alternative readings and relevant contributions to share with class. You will be graded each week on your participation under the following criteria:

  • Ability to generate questions for class: This should be at least two or three questions based on recommended readings and/or other materials you find. These questions should be relevant to the topics covered each week.
  • Sharing materials online
  • Online discussion: You are required to engage in discourse online through the Canvas portal

Final grades will be determined on a 0-100 scale. Your final course grade will be determined as follows: 94-100=A; 90-93.999=A-; 87-89.999=B+; 84-86.999=B; 80-83.999=B-; 77-79.999=C+; 74-76.999=C; 70-73.999=C-; 60-69.999=D; below this is an F. Once issued, all grades are final and will not be changed. Borderline grades will be reviewed on a case-by-case basis. If you have questions about how grades are assigned in this course, please bring them up at the beginning of the semester or soon thereafter. No questions concerning grading policy will be considered once grades have been submitted.


Lecture Outline

Topic Reading
Week 1 Course Introduction 10 Kinds of Stories to Tell With Data

The Dataviz Design Process The Value of Visualization Visions and Re-Visions of Charles Joseph Minard Core Principles of Data Visualization

Week 2 Developing a Design Why Data Visualization Matters

Mind Mapping The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations Five Models for Making Sense of Complex Systems 5 Simple Techniques for Powerful Data Storytelling An Exploratory Study of Data Sketching for Visual Representation

Week 3 Review of Assignment 1: Design and Visualization; Data Modelling The Ben Fry Visualizing Data Process

Voyagers and Voyeurs: Supporting Asynchronous Collaborative Information Visualization Things that make us smart.

Week 4 Interactivity and Tools Overview; Data Visualization Collaboration Data Organization in Spreadsheets

Learning Visual Importance for Graphic Designs and Data Visualizations

Week 5 Review of Assignment 2: Data Exploration; How the London Whale Debacle is Partly the Result of An Error Using Excel

Data’s Credibility Problem Review of​ The Guardian​, ​FiveThirtyEight 30 Resources to Find the Data You Need

Week 6 Group Data Product; Perception in Visualization Data Visualization Effectiveness Profile

The Visual Perception of Variation in Data Analysis What’s visual ‘encoding’ in data viz, and why is it important?

Week 7 Review of Assignment 3: Interaction Visualization; Design Principles and Narratives Makeover Monday: Lessons

Alberto Cairo's ​tips​ for more effective data visualization Storytelling: The Next Step for Visualization Data Visualization: Clarity or Aesthetics

Week 8 Map Visualization Beyond Basic R - Mappin​g

Robert Simmon: Subtleties of Color, Parts 1-6 How to Lie with Maps: A practical Example

Week 9 Review of Assignment 4: Map Visualization Adaptive Composite Map Projections

A Guide to Selecting Map Projections for World and Hemisphere Maps The Open Data Handbook

Week 10 Networks and Deconstructions Network Visualization of Ryan Hoover on Twitter

#QAnon Network Visualizations Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data Detecting criminal organizations in mobile phone networks

Week 11 Individual Final Project Proposal Submission
Week 12 “Advanced” Topics
Week 13 Final Project Proposal Design Review
Week 14 Team Project Presentations