CS 3891.01 / 5891.01 - Data Visualization

Overview Syllabus Schedule Assignments Project Resources


Syllabus

Course Description

Suppose you were provided a dataset, say a table of numbers, and you were interested in performing a set of analyses on the data. For instance, you might be interested to know if there exists trends, correlations, or outliers in the data. How does one conduct data analysis? One way is to perform statistical analysis, in order to test for these considerations. Yet statistical techniques might only tell a very small story about the data, for instance a single number and confidence bounds. Furthermore, performing some statistical analysis presumes that you know what to test. What if your analysis objectives are a bit fuzzy, and you cannot precisely state what you want to compute?

Data visualization is concerned with helping users make sense of data. Within the context of data analysis, visualization is useful as an aid for users in support of existing analyses, helping people be more efficient, and more quickly arriving at conclusions. But visualization can also be useful as a form of knowledge discovery, noticing something that you never would have thought of were you to not visualize your data.

In this course you will learn how to create effective visualizations, those that are suited for an end user’s goals. To create an effective visualization:

  1. We first require an understanding of how to visually encode data. Many software packages, tools, and libraries exist that offer accessible abstractions for users to create a visualization. In this course, you will learn the fundamentals on how we map a data item to its graphical mark, what is ultimately drawn on the screen. In this way, you will have full control over how to design a visualization, without adhering to existing libraries that implicitly make design choices for you.
  2. We secondly require an understanding of design decisions. Visualization design is all about the choices that we make on how we visually encode data. This can be quite daunting! How do we know we are making the best decisions? You will learn how an understanding of visual perception, coupled with the goals of the user, can help us narrow the decisions that we should make, to the point that the visualizations we create seem almost self-evident.

Learning Objectives

The set of topics that you will learn are broken down into three main sections: Developing Visualizations, Design Principles and Visualization Techniques. These topics will be interspersed throughout the semester.

Developing Visualizations

You will learn how to write code for authoring data visualizations. You will learn the following technologies:

These technologies can go pretty deep - you will only need to learn what is necessary for visualization development.

Design Principles

As you will quickly learn, visualization design is all about choices. A working knowledge of design principles will help you make good choices, those that are optimized for human perception, and the goals that the user has in mind when using a visualization.

Visualization Techniques

As visualization design is all about choices, we need to know what choices are available to us! Some of these choices are rather straightforward (e.g. a scatterplot), but others are specific to certain types of data (e.g. a graph). Thus, you will learn about visualization techniques.

Prerequisites

CS 3250/3251, MATH 2410 or 2501 or 2600, MATH 2810 or 2820

More specifically, data visualization is inherently an interdisciplinary research field, as it relies on understanding the data at hand. That said, having a good understanding of data structures and algorithm design should enable you to (1) grasp the types of data we will work with, and (2) how to approach implementing visualization algorithms.

Now, another important aspect of visualization is space. Space is utilized in a myriad of ways in constructing data visualization. So you should be familiar with 2D geometric computing, as would be covered in linear algebra, in order to work with spatial transformations, scales, distances, and shapes. We will cover these basics throughout the course.

Instructor

Matthew Berger

email: matthew.berger@vanderbilt.edu

Office: Sony building, Rm 4028

Office hours: TR 2:00-3:00

Teaching Assistant

Sangwon Jeong

email: sangwon.jeong@vanderbilt.edu

Office: Sony building, Rm 4018

TA office hours: W 11am-12pm, F 10am-11am

Discussion

We will use Brightspace for any discussion related to the course: questions on lecture content, programming assignments, etc..

Course Format

The course will consist of lectures, a set of programming assignments, a set of written assignments, a project, and a final exam.

The lectures will typically be of two formats, what I call theory and practice. In the theory portions of lectures, I will discuss the basic concepts behind visualization; e.g. data representations, perceptual principles, design guidelines, and visualization techniques. The practice portions of lectures will complement the theory by covering how to actually implement the ideas that are discussed. The practice format will largely be focused on the details of D3, discussing how to use D3 for particular purposes, accompanied by sets of examples.

The lectures will draw from the textbooks mentioned below, as well as research papers.

Programming assignments will allow you to practice the implementation of visualization designs and techniques that will be covered during lectures.

Written assignments will provide you with practice in analyzing existing visualization designs - formalizing design decisions, and performing design critiques.

There will, further, be a project associated with the course, towards the latter half of the semester. You will work in teams of size 2-3. For the project, you will be expected to complete a design study: for a given dataset, you will create a set of objectives that comprise what you would like to learn about the data, a set of tasks that address these objectives, and design a visualization built around these tasks. More information is available on project.

The final exam is meant to test your knowledge on everything that is covered during lectures, with an emphasis on design principles and techniques.

Textbooks

Required

Visualization Analysis and Design by Tamara Munzner. This book will serve as the core for discussing visualization principles and techniques. Copies have been reserved in the book store; if not available, you can always purchase it online.

Interactive Data Visualization for the Web by Scott Murray. This book covers the basics behind D3, and its purpose is to get you to understand how to code data visualizations. If you are already familiar with D3, or are comfortable using the myriad of D3 resources on the web (see Resources), then you might not find this book necessary, otherwise I strongly recommend picking it up.

Optional

Design for Information by Isabel Meirelles. This book covers the basic principles in how to design visualizations for a variety of data types, along with abundant examples.

Making Data Visual by Danyel Fisher and Miriah Meyer. This book covers the why behind visualizing data, namely, how do tasks drive our decisions in designing visualizations.

Lectures

MW, 8:40am-9:55am, Featheringill Hall 258

Lecture Slides

See schedule.

Assignments

See assignments.

Assignments will be posted and introduced during class, and will be due at 11:59:59pm on their respective due dates. You will use Observable notebooks to complete assignments - programming and written - and Brightspace will be used to submit assignments. Please refer to assignments for more information.

For most programming assignments, graduate students will be expected to complete more advanced topics, which will typically serve as extra credit for undergraduates.

Resources

See resources.

A note on Observable notebooks

Observable notebooks will serve as the main programming environment for the course, and how you will complete assignments and the course project. But more than that, Observable notebooks foster interactivity and collaboration.

See further details on Observable notebooks in assignments, as well as resources. We will also cover the basics of Observable during lectures.

Assessment

Grades

Your final grade will be numeric, and will be converted into a letter grade via the following (upper bounds are exclusive):

Late Submission Policy

If an assignment is not submitted on its due date, it may still be submitted within the proceeding 24 hours, but the submission will receive a 15% penality. Beyond 24 hours, no credit for assignments will be given. Exceptions will be made depending on health/safety of students throughout the semester (see Covid-19 Policies below).

Regrading Policy

If you have disagreements with respect to grading of assignments, exams, or the project, you will have one week after having received it to dispute the grade. You may submit a written request for a reassessment, and I will then review the request, determine whether or not to regrade the assignment/exam/project, and perform the reassessment within one week of receiving the request.

Covid-19 Policies and Guidance

Classroom Restrictions

It is a requirement to wear a mask during lectures.

If you have tested positive for Covid-19, then you should not attend class. If I test positive for Covid-19, then I will not attend class. In these instances, lectures will be held remotely. If my illness prevents me from holding lectures remotely, then substitute/guest instructors will hold lectures instead.

If you come down with the virus, and are unable to keep up with progress, then please let me know. We will coordinate a schedule that will enable you to be successful in this course. Your health takes priority over this course, so do not feel that you need to “push through” this course if you are feeling unwell.

Mental Health During Covid

If you begin to experience any kind of anxiety, please know there is plenty of support available at the university: the Center for Student Wellbeing, University Counseling Center, and Student Health Center. I encourage you to take advantage of these resources.

Department and University Academic Policies

Academic Honesty

Students should adhere to the Vanderbilt Honor System. Cheating or plagiarizing will not be tolerated in this course.

Academic Integrity

More generally, students should act in accordance with the academic integrity policies of the university, please see Vanderbilt’s Academic Integrity for more information.

Privacy

All student data and information will be protected under FERPA laws. Please refer to the Vanderbilt Student Privacy Statement. Please take care to not disclose any private information during lectures and when submitting assignments.

Nondiscrimination and Anti-Harassment

Vanderbilt is committed to an environment that is free of discrimination and harassment of any kind. There is zero tolerance for discrimination/harassment of anyone based on the color of their skin, or the gender that they identify with. If you are Black / African American and feel that you are the target of racial discrimination, please reach out to the University Counseling Center. If you feel that you are being harassed based on your identified gender, please reach out to Project Safe.

Subject to Change Statement

Information contained in the course syllabus, other than the general assessment, may be subject to change with advance notice, at the discretion of the instructor.