Data Science and Visualization

Spring 2019
Mini-4 T/H 3:00-4:20pm in Gates 4301
Picture of Adam Perer
Adam Perer
(Instructor)

The volume and complexity of data continues to increase in the world around us, including science, business, medicine, social media and everyday human activity. This course aims to expose students to data science and visual representation methods and techniques that increase the understanding of complex data. Proper data science and visualizations will not only present an interpretation of data, but do so by improving comprehension, communication, and decision making. In this course, students will learn about the fundamentals of data science, machine learning, perception, the theory of visualization, and good design practices for data visualization. The course will also provide hands-on experience on the process of data communication, from initial data analysis, to identifying appropriate data science techniques, to crafting informative visualizations using tools.

More details on the Schedule and Projects is available for enrolled students on Canvas.

Syllabus

Course Goals

The volume and complexity of data continues to increase in the world around us, including science, business, medicine, social media and everyday human activity. This course aims to expose students to data science and visual representation methods and techniques that increase the understanding of complex data. Proper data science and visualizations will not only present an interpretation of data, but do so by improving comprehension, communication, and decision making. In this course, students will learn about the fundamentals of data science, machine learning, perception, the theory of visualization, and good design practices for data visualization. The course will also provide hands-on experience on the process of data communication, from initial data analysis, to identifying appropriate data science techniques, to crafting informative visualizations using tools.

This course will also focus on small data, by which I mean datasets that fit within a spreadsheet or within a computer’s memory. It will focus on techniques rather than on technologies. While software is an essential part of data science and visualization, the goal of this course is to focus on analyzing and communicating information to people, and specific software is merely one means to an end.

At the end of this course, you will:

  • Be able to analyze a dataset, evaluate potential insights, and identify specific questions.
  • Be able to choose appropriate software and other technology for data science given your personal abilities and the goals of the project.
  • Have a demonstrable understanding of visualization techniques, types of charts and graph techniques, color theory, and human visual perception.
  • Be able to communicate specific messages with data and to use data persuasively. Have a working ability to obtain, analyze, manipulate, transform, and distribute data.

Work Required

This will not be an exam-heavy course. Instead, much of the work will focus on projects. The course will focus on understanding the techniques of data science and visualization through developing creative analyses and visualizations using tools to solve defined problems.

There is no final exam or final project in this course. Students who do well will be invited to continue on an independent project on topics related to the course, working with Prof. Perer during a future semester.

Grading

We intend for anyone who puts in the effort required to be able to achieve a B or better in this course. Projects are intended to be straightforward to complete and/or prepare (albeit require significant effort).

Late Policy

You can turn in your assignment up to two days late, however, for each day that an assignment is turned in late we will deduct 10% off the total possible points. That is, one-day late is 10% off, two-days is 20% off. For example, if your assignment is two day late, the max number of points (out of 10) that you can receive is 8. By permission of the instructor in extenuating circumstances, you may use more that two late days, however, the 10% rule per day will still apply. If you have a verifiable medical condition or other special circumstances that interfere with your coursework, please let us know as soon as possible.

Attendance

Attendance is mandatory. If you must miss class for illness, family emergency, or other valid reason, please let me know before the class to make arrangements.

Laptops

Laptops will be permitted in class, and will be required for use during technology workshops. Please be respectful of the class and your classmates with your use of laptops by avoiding distracting content.

Office Hours Please join me and the graders for office hours. My normal office hours will be TBD in Newell-Simon Hall (second floor). Please also feel free to send a message to him on Slack.

If you are asking a general question, that other students may also benefit from seeing, please ask in a Canvas discussion. If you are asking a question specific to you, e.g., about a grade you received, about absence from class, an accommodation request, etc., then please ask us individually either in person or via email.

We try to respond very quickly, but please do email me again if you don’t receive a response within 24 hours.

Collaboration, Cheating and Plagiarism Homework must be individual work unless otherwise stated. You are encouraged to consult each other on clarification, technical and conceptual issues, and on interpreting the data but you must do individual problem solving and derive your own solutions, including your own computer and design work. If you have any question concerning whether an act is appropriate please consult me or the appropriate university official before acting. The minimum penalty for cheating on an assignment is zero credit for the work submitted, and the maximum penalty is being failed for the course.

You are responsible for being familiar with the university standard for academic honesty and plagiarism. Please see the University Student Handbook for information. In order to deter and detect plagiarism, online tools and other resources are used in this class.

Accommodations for Students with Disabilities If you have a disability and have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at access@andrew.cmu.edu.

Health and Well-being

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help. If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night: CaPS: 412-268-2922 Re:solve Crisis Network: 888-796-8226 If the situation is life threatening, call the police On campus: CMU Police: 412-268-2323 Off campus: 911

If you have questions about this or your coursework, please let me know. Thank you, and have a great semester.

(Some of this helpful content was borrowed from Prof. Jeff Bigham’s Human-AI Interaction Course)