SYLLABUS 2024

Author

Barrie Robison

Published

January 12, 2024

BCB 520: Foundations of Data Visualization

Barrie D. Robison

Spring 2024

Course Description

This class will help students establish a core understanding of data visualization. We will consider how data type (including tabular, network, and spatial data) interacts with visualization task to guide design choices. Diverse types of visual encodings and how they relate to human perception will be presented, along with practical exercises using the R programming language. Upon completion of the course, students will understand WHY particular visualization approaches are effective for a given data set and HOW to implement those visualizations using R. The course is designed to be “discipline agnostic” - each student is encouraged to use data sets that they deem important / interesting. The goal is to have students learn how to develop visualizations that are relevant to their own disciplinary interests.

Course Objectives

Students completing this course will be able to:

  1. Describe and manipulate tabular, network, and spatial data; transform these data into a form suitable for visualization.
  2. Analyze data visualization design choices related to marks and channels, spatial arrangement, and components of color.
  3. Design new data visualizations with appropriate use of visual channels for tabular, network, and spatial data with quantitative and categorical attributes.
  4. Implement their data visualization designs using existing tools in R (or other toolkits preferred by the student).
  5. Explain whether a visual encoding is perceptually appropriate for a specific combination of task and data.
  6. Demonstrate their skills with at least two novel visualizations suitable for inclusion in an online Data Science Portfolio.

Required Readings

Tamara Munzner. Visualization Analysis and Design. A K Peters Visualization Series, CRC Press, 2014. While the book is not required, I do emphasize the structure and approach to visualization that Dr. Munzner has developed.

Hard Copy on Amazon

kindle/ebook on Amazon

Course Policies

GRADING

50% of your grade will be determined by homework exercises related to each course unit.

20% of your grade will be determined by a mid term project (which would be a great item to include in your Data Science Portfolio).

20% of your grade will be determined by a final project (which would be great item to include in your Data Science Portfolio).

10% of your grade will be determined by participation in class discussions.

GRADING SCALE: The grading scale is standard: A (90 -100 %), B (89 - 80 %), C (79 - 70 %), D (69-60 %), F( below 60 %).

ATTENDANCE POLICY

Missing a scheduled class session is at your discretion. I will be posting all the course materials online. If a discussion or in-class exercise occurs and you miss it, you will lose those participation points. There is no way to make up those points.

LATE ARRIVAL OF PROFESSOR POLICY

The R Markdown template I used for this syllabus was created by Dr. Steven V. Miller at Stockholm University. It contained this section, which I found amusing and have therefore retained. Professor Miller’s current university asks professors to have policies written into their syllabus about what students should do if the professor is more than 15 minutes late to class. Here is my version of that policy:

I will inform students via e-mail in advance of class if class is cancelled for the day. Events that might create such a scenario include travel obligations that emerged after the semester has begun, a family emergency that encompasses multiple days, or some other thing. I will also contact our department secretary in emergent situations, such as something happening on the way to work. Failing that, assume the worst. Alien abduction, the return of one or more Old Ones to our plane, or some kind of attack by wizards are all viable explanations for my inability to attend class. I ask that the students make sure that my story gets the proper treatment on the “Mr. Ballen” YouTube channel. I also ask that my story be narrated by Morgan Freeman and that the role of me in the made for TV movie be played by Keanu Reeves or Danny DeVito.

MAKE UP EXAM POLICY

The bad news is that there are NO make-ups for missed exams. Don’t bother asking. The good news is that there aren’t any exams.

ACADEMIC DISHONESTY POLICY

All students are expected to uphold the highest standards of academic honesty. This includes but is not limited to: not cheating, not using the ideas of others without giving appropriate credit (including AI tools), and not falsifying data. Any incident of academic dishonesty will be handled according to the guidelines of the University of Idaho.

Class Schedule

library(readxl)
Schedule <- read_excel("Schedule.xlsx")

knitr::kable(Schedule, caption = '')
DATE TOPIC ACTIVITY RESOURCES
2024-01-11 Introduction and Overview NA NA
2024-01-16 The Imporance of Visualization Literate Programming Tutorial 1, Tutorial 2
2024-01-18 NA NA NA
2024-01-23 NA NA NA
2024-01-25 NA NA NA
2024-01-30 WHAT?  Abstraction of Data NA NA
2024-02-01 WHY?  Task Abstraction NA NA
2024-02-06 NA NA NA
2024-02-08 MARKS. Geometric elements to depict data NA NA
2024-02-13 NA NA NA
2024-02-15 NA NA NA
2024-02-20 NA NA NA
2024-02-22 CHANNELS. Controlling the appearance of marks. NA NA
2024-02-27 RULES OF THUMB. Midterm Presentations NA
2024-02-29 TABULAR DATA I Midterm Presentations NA
2024-03-05 TABULAR DATA II NA NA
2024-03-07 SPATIAL DATA I: Geographic Maps NA NA
2024-03-12 Spring Recess NA NA
2024-03-14 Spring Recess NA NA
2024-03-19 Barrie in Vermont NA NA
2024-03-21 SPATIAL DATA II:  Spatial Fields NA NA
2024-03-26 NETWORK DATA I NA NA
2024-03-28 NETWORK DATA II NA NA
2024-04-02 COLOR I NA NA
2024-04-04 COLOR II NA NA
2024-04-09 COLOR III NA NA
2024-04-11 INTERACTIVITY NA NA
2024-04-16 MULTIPLE VIEWS NA NA
2024-04-18 AGGREGATION NA NA
2024-04-23 FILTERING NA NA
2024-04-25 EMBEDDING: Focus and Context NA NA
2024-04-30 DEAD WEEK Final Presentations NA
2024-05-02 DEAD WEEK Final Presentations NA
2024-05-07 FINALS WEEK NA NA