Data Visualization

Learning objectives

At completion of this course, students will be able to:

1. Understand data theory

2. Be familiar with principles behind effective data visualization

3. Read complex data through educated eyes

4. Communicate message in data effectively using advanced visualization techniques

5. Build data visualization applications

Overview

Readings

There will be a lot of readings in this class.  You will advance from general understanding of data to scientist level of understanding data.  

Learn how to read long data science books

Policy on using generative AI

There are generally three policies on governing using generative AI in course setup:

1. Prohibiting AI Use

2. Allowing AI Use Without Restrictions

3. Permitting AI Use with Disclosure

Policy on using generative AI

1. Michigan
   
   "In principle you may submit AI-generated code, or code that is based on or derived from AI-generated code, as long as this use is properly documented in the comments: you need to include the prompt and the significant parts of the response. AI tools may help you avoid syntax errors, but there is no guarantee that the generated code is correct. It is your responsibility to identify errors in program logic through comprehensive, documented testing. Moreover, generated code, even if syntactically correct, may have significant scope for improvement, in particular regarding separation of concerns and avoiding repetitions. The submission itself should meet our standards of attribution and validation.

Policy on using generative AI

2. Harvard

Certain assignments in this course will permit or even encourage the use of generative artificial intelligence (AI) tools, such as ChatGPT. When AI use is permissible, it will be clearly stated in the assignment prompt posted in Canvas. Otherwise, the default is that use of generative AI is disallowed. In assignments where generative AI tools are allowed, their use must be appropriately acknowledged and cited. For instance, if you generated the whole document through ChatGPT and edited it for accuracy, your submitted work would need to include a note such as “I generated this work through Chat GPT and edited the content for accuracy.” Paraphrasing or quoting smaller samples of AI generated content must be appropriately acknowledged and cited, following the guidelines established by the APA Style Guide. It is each student’s responsibility to assess the validity and applicability of any AI output that is submitted. You may not earn full credit if inaccurate on invalid information is found in your work. Deviations from the guidelines above will be considered violations of CMU’s academic integrity policy. Note that expectations for “plagiarism, cheating, and acceptable assistance” on student work may vary across your courses and instructors. Please email me if you have questions regarding what is permissible and not for a particular course or assignment.

Policy on using generative AI

3. Carnegie Mellon University

You are welcome to use generative AI programs (ChatGPT, DALL-E, etc.) in this course.  These programs can be powerful tools for learning and other productive pursuits, including completing some assignments in less time, helping you generate new ideas, or serving as a personalized learning tool.

However, your ethical responsibilities as a student remain the same. You must follow  CMU’s academic integrity policy. Note that this policy applies to all uncited or improperly cited use of content, whether that work is created by human beings alone or in collaboration with a generative AI. If you use a generative AI tool to develop content for an assignment, you are required to cite the tool’s contribution to your work. In practice, cutting and pasting content from any source without citation is plagiarism. Likewise, paraphrasing content from a generative AI without citation is plagiarism. Similarly, using any generative AI tool without appropriate acknowledgement will be treated as plagiarism.

Policy on using generative AI

4. UTD (some courses)

Cheating and plagiarism will not be tolerated. 

The emergence of generative AI tools (such as ChatGPT and DALL-E) has sparked large interest among many students and researchers. The use of these tools for brainstorming ideas, exploring possible responses to questions or problems, and creative engagement with the materials may be useful for you as you craft responses to class assignments. While there is no substitute for working directly with your instructor, the potential for generative AI tools to provide automatic feedback, assistive technology and language assistance is clearly developing. Course assignments may use Generative AI tools if indicated in the syllabus. AI-generated content can only be presented as your own work with the instructor’s written permission. Include an acknowledgment of how generative AI has been used after your reference or Works Cited page. TurnItIn or other methods may be used to detect the use of AI. Under UTD rules about due process, referrals may

be made to the Office of Community Standards and Conduct (OCSC). Inappropriate use of AI may result in penalties, including a 0 on an assignment.

Policy on using generative AI

Using generative AI may not save time, but will improve quality and deepen thought process.

As a “language” for the eye, graphics benefits
from the ubiquitous properties of visual perception. "

- Jacques Bertin 

Objectives:

1. Understand data theory

2. Be familiar with principles behind effective data visualization

3. Read complex data through educated reviews

4. Communicate message in data effectively using advanced visualization techniques

• Data visualization is to deliver a message from your data.

• It is like telling a story using the chart or data applications.

• Sometimes the data is huge or the story to too long to tell.

• Visualization provides an ability to comprehend huge amounts of data. The important information from more than a million measurements is immediately available.

• Learn to read your data

  • Visual thinking
  • Educated eyes

Data visualization is to communicate data patterns, findings and insights via visual representation of data.  It is well beyond just creating a chart but to train "Thinking Eyes" and build data literacy.

How much information does this picture present?

Messages:

• Gradual rise?   

• Abrupt Drop of Ranking?

Source: Chris Adolph, also Johnson, R.R. and Kuby, P.J., 2011. Elementary statistics. Cengage Learning.

Message:

• Age structure of college enrollment

• How much data are presented in multiple colors?

Source: Edward R. Tufte. 2001. The Visual Display of Quantitative Information. Graphics Press. 2nd ed.

1. Numeric data

   1. Scale

      1. Nominal 
      2. Ordinal
      3. Interval
      4. Ratio
2. Categories
3. Events
4. Time series

William Cleveland