Syllabus

Course Details

Semester Fall 2024
Section PSYC 167
Day Time T/R 1:15-2:30 PM or 2:45-4:00PM (Pacific)
Location Location: Roberts South, 102
Office Hours T/R: 12-1pm; Adams 110
Instructor Gabriel I. Cook
Contact Discord (preferred) or Email: gcook@CMC.edu (please put ’PSYC 167 in subject line)
Credit 3 hours; 1 credits

Course Description

Data visualization is the science and art of creating graphical representations of information and data. Visual representations provide accessible ways to see patterns, trends, and outliers in data. Variables like position, size, and orientation can focus attention and guide perception but can also bias interpretation of data. Students will learn how well-designed visualizations can reduce bias and improve comprehension for data thereby facilitating data-driven decision-making. Students will explore techniques for creating effective visualizations based on principles from cognitive and perceptual psychology, art, and design. Students will gain hands-on experience coding real-world data visualizations for local offices, organizations, and industry participants.

The course is targeted toward students with expressed interest in cognition and cognitive biases related to data communication, students interested in using visualization to communicate their own messages, and students interested in creating better visualization tools and systems. Students will engage in discussions of the readings, complete programming and data analysis assignments, and prepare a final project involving storytelling with data visualizations.

Prerequisite: For data-science sequence or majors (level-A data-science course); recommended a course in Perception, Visual Attention, Cognitive Psychology, or Cognitive Science; or permission of instructor

Course Specific Learning Goals

  • Understand various uses of visual variables to create data visualizations;
  • Understand both advantages and disadvantages of using visual variables to create data visualizations;
  • Analyze, critique, and revise data visualizations;
  • Understand the functionality of the ggplot2 library for creating data visualizations;
  • Present data with visual representations for your target audience, task, and data;
  • Identify appropriate data visualization techniques given particular requirements imposed by the data and/or audience; and
  • Apply appropriate design principles in the creation of presentations and visualizations

The following departmental learning goals will also be met: 1. Knowledge of major concepts in cognitive psychology; 2. Understanding of research methods in psychology, including research design, data analysis and interpretation; 3. Development of critical-thinking skills and use of the scientific approach to solve problems related to behavior and mental processes; 4. Oral and written communication skills.

Courses at CMC

Faculty Handbook 5.4.2 Work Load in Classes

“Courses should involve approximately equal workloads. Generally, students should expect to spend from 6 to 8 hours per week, over and above the time spent in classroom, on each course.” – CMC Faculty Handbook

If you do the math, including class time of 2½ hours, you should expect to allocate 8 to 10 hours per week for courses at CMC. “Per week” is a key phrase; courses are not designed for nondistributed cramming.

Course Materials and Textbook

All of the course materials will be available on this course website .

Link to the course website: https://gabrielcook.xyz/dataviz24/

Required Equipment:

Computer: current Mac (macOS) or PC (Windows or Linux) with high-speed internet connection, capable of running R and RStudio; hard-drive space about 5 GB

Required Software:

R and RStudio: Students will be required to use R and RStudio software. Note: Install Version will be provided. Before installing RStudio, you must also download and install the base R software at https://www.r-project.org/ that is appropriate for your computer’s operating system. RStudio can be downloaded for free at https://www.rstudio.com. You are expected to install R and RStudio on your personal computer by downloading the software from the links above. You will also have to install appropriate libraries throughout the course. Further instructions will be provided.

Reading Materials/Textbook(s)

Reading materials will be represented in the course modules and will be referenced there in. Some topics will require reading external to the module content, so make sure to check and plan accordingly.

Other free and open-source materials on the topic that you might find interesting include:

Course Structure

Students are expected to participate in all aspects of the class. This class involves developing topic knowledge and computer programming skills for visualizing data. The assumption is that students possess varying levels of skills related to programming. Class time will be spend engaging in a variety of tasks and activities, including lectures, group-work, applied coding activities, presentations, and discussions.

These textbooks are free and open-source.

Overview

Students will read materials covering data-set relevant cognitive functions or abilities and tasks or tools used to measure those abilities. They will also will learn about coding in R, data validation and wrangling, and support their current knowledge of statistical probability and inference.

Coding for Data Science: Students will be introduced to functional programming using R, application of models, and use of popular data-science libraries, (e.g., dplyr, ggplot, stringr, etc.). Students would learn elements of programming (e.g., assignment, functions, function arguments, operators, objects, passing objects, control flow, etc.).

Data Validation and Wrangling: Students will learn how to wrangle raw data, clean, and manipulate data. The course would involve both data wrangling and data cleaning. Students would learn main concepts of data sanitation of messy data, for example, how to clean, recode, de-dup, fix structural errors and typos, standardize data, etc. in service of applying machine-learning models.

Project Management: Projects for academics and industry involve collaboration as well as organization of code and materials. Students will learn about and maintain a project with an organized directory structure both locally and remotely with collaborators using Git and GitHub.

Academic Integrity. Although you may find yourself working on assignments with a partner or discussing them with classmates, all assignments should be your one original work. You are not to share materials with other students if that material has the potential of being copied, even if your intention is not to allow a classmate to copy your work. Any signs of academic dishonesty will be submitted to the Academic Standards Committee for review. Although I do not anticipate any events of academic dishonesty, any form of dishonestly of any form will not be tolerated.

Many students are unclear of the definition of plagiarism and for that reason I have posted some CMC links to information that I believe will clarify the issue. In addition, any work completed for another course, past or present, may not be submitted for a grade for this course. http://registrar.academic.claremontmckenna.edu/acpolicy/default.asp

Course Modules. This course will be split into modules, allocating various weeks depending on the scope of the module.

Course Structure

Students are expected to participate in all aspects of the class. This class involves developing topic knowledge and computer programming skills for visualizing data. The assumption is that students possess varying levels of skills related to programming. Nevertheless, students are expected to attend class prepared to engage with and practice concepts related to readings and lectures. Prior to class, students should have completed course content (e.g., videos, modules, or readings referenced therein) and watched any associated lectures on the material. Class time will involve answering questions raised by students, a mini lecture, and coding activities that will inform the final project (note, concepts build). Homework assignments will also involved engagement with the project data. Class time will be spent engaging in a variety of tasks and activities, including lectures, group-work, applied coding activities, presentations, and discussions.

Course Schedule

Calendar Date Week Day Module Content Homework Assignment Knowledge Assessment
Due By First Class Installation & Setup
27-Aug T Introduction
29-Aug R Project Management (Parts 1 and 2)
03-Sept T Graphical Perception
05-Sept R Data Frame Manipulation and Wrangling
10-Sept T Data Subsets and Summaries
12-Sept R Considerations in Data Visualization
17-Sept T Designing Perceptually Efficient Visualizations HW 01 KA 1
19-Sept R The Grammar of Graphics
24-Sept T Visualizing Associations HW 02
26-Sept R Spatial Position and Adjustment
01-Oct T Visualizing Amounts HW 03 KA 2
03-Oct R Color Scales and Palettes
08-Oct T Histograms and Density Plots HW 04
10-Oct R Coordinates, Axes and Position Scales
15-Oct T Fall Break (no class)
17-Oct R Statistical Transformations (Data as-is Versus Summaries)
22-Oct T More Data Wrangling HW 05 KA 3
24-Oct R Mid-Term Presentation
29-Oct T Visualizing More Distributions HW 06
31-Oct R Visualizing Uncertainty
NLT
31-Oct		 Mid-Term Presentation to Liaison (By End of Day)
05-Nov T Legends and Arrangement HW 07 KA 4
07-Nov R Visualizing Trends
12-Nov T Multi-Panel Plots: Faceting and Layers HW 08
14-Nov R Annotation and Text
19-Nov T Attentional Control and Tradeoffs HW 09 KA 5
21-Nov R Titles Captions & Tables
26-Nov T [online] Catch-Up, Figure Design (Themes), or Team Project Preparation HW 10
28-Nov R Thanksgiving Break (no class)
03-Dec T Catch-Up or Team Project Preparation
05-Dec R In-Class Presentation (Last day of Class)
NLT
13-Dec		 Fri Finals Week: Final Push to Repo/Written Report (By Noon) MU
NLT
13-Dec		 Fri Final Presentation to Liaison (By End of Day) - Do Not Schedule your departure prior to presenting to your project’s organization, otherwise expect a grade deduction. The semester schedule and final-exam schedule were available during registration time.
Note(s): Assessments will take place during the first 10 minutes of class time. If you are late and cannot finish in the time allotted, see below.
There will be 1 optional make-up/replacement assessment (MU) offered which can replace a missed assessment or homework or unsatisfactory assessment or homework. The percentage value (not point value) will be applied to items of unequal point value.
NLT = No Later Than

Assignments and Grading

This is an engagement and skills-acquisition based course. Students will have opportunities to strengthen skills outside of the classroom in preparation of performing class exercises related to the team project, take computer-free quizzes that involve writing and evaluating code and objects returned from code. Failure to engage in material or rely on ChatGPT or other AI tools will likely result in an inability to perform well on quizzes. The team project involves weekly data manipulation and visualization as well as presentations and a final deliverable.

Students are expected to make progress weekly on the team project by completing projects elements that are currently possible to complete (e.g., organizing the repo and its directories, editing the R Markdowns files, ensuring reproducibility, drafting background content, etc.). Procrastination on your part will bring you unnecessary stress and anxiety, a lower quality deliverable, and diffusion of responsibility that I was somehow responsible for your oversight. In turn, your frustration may result in lower course evaluations for me. I am fine with this.

Evaluation and Grading

Item Total Points (%) Points
Readings & Videos (by class time) 5% 20
In-Class Knowledge Assessments 20% 80
Conceptual and Programming Homework 20% 80
Midterm Presentation 20% 80
Final Project (Pres and Report) 35% 140

Percentage grades are converted to letter grades according to the following rubric.

Letter % Range
A 94 - 100
A- 90 - 93.99
B+ 87 - 89.99
B 84 - 86.99
B- 80 - 83.99
C+ 77 - 79.99
C 74 - 76.99
C- 70 - 73.99
D+ 67 - 69.99
D 64 - 66.99
D- 60 - 63.99
F 0 - 59.99

Attendance

Students are expected to attend and participate in each class. If you are unable to attend class, you need not provide any rationale or excuse. You are, however, responsible for submitting homework that is due for that day and for accepting the responsibility for missing assessments on that day. Class time will be used to address coding concerns, work through project-related exercises with your team, and practice coding to build skills. If you elect not to participate, please understand that decision will be reflected in class performance related to quality, professionalism, team contributions, etc.

Course Policies

Due dates

Due dates are suggestions for completing coursework on a weekly basis. There is a lag with homework assignment content so that you are able to complete them early if desired. You may be able to work ahead but you are not encouraged to fall behind. Late homework will be reduced at 50%.

You should email me if you have an exceptional circumstance preventing you from taking an assessment during an assessment week.

Changes to the syllabus

The syllabus may be updated for clarity or to make adjustments for pedagogical purposes. The most current version of the syllabus is always available from the course website.

Accessibility

I have a disability and understand your needs. In order to receive disability-related academic accommodations students should contact me for arrangements as per instruction from disability services.

Correspondence

I may on occasion need to use e-mail but you should contact me on the Discord channel, which is where I will post announcements, changes, reminders, etc. You are responsible for monitoring Discord and e-mail regularly.

If you have questions, please message me on Discord.

If you need to e-mail me:

  1. Always add ’PSYC 167” to the subject line
  2. email me at: gcook@cmc.edu

College Policy on Academic Integrity

The faculty and administration of Claremont McKenna College support an environment free from cheating and plagiarism. Each student is responsible for being aware of what constitutes cheating and plagiarism and for avoiding both.

Violations of Academic integrity

Each student is responsible for understanding and acting in accordance with the College’s policy on Academic Integrity, described below.

Academic Integrity

Although you may find yourself working on assignments with a partner or discussing them with classmates, all assignments should be your one original work. You are not to share materials with other students if that material has the potential of being copied, even if your intention is not to allow a classmate to copy your work. Any signs of academic dishonesty, even those raised by concerned peers, will be submitted to the Academic Standards Committee for review. Although I do not anticipate any events of academic dishonesty, any form of dishonestly of any form will not be tolerated. Many students are unclear of the definition of plagiarism so I have posted some CMC links to information that I believe will clarify the issue. In addition, any work completed for another course, past or present, may not be submitted for a grade for this course and would be a violation of integrity. http://registrar.academic.claremontmckenna.edu/acpolicy/default.asp

Statement of Reasonable Accommodations

I have an eye disease and visual impairment and understand the need for accommodations. Your experience in this class is important to me. If you have already established accommodations with Disability & Accessibility Services at CMC, please communicate your approved accommodations to me during the first week of the semester so we can discuss your needs in this course ASAP. You can start this conversation by forwarding me your accommodation letter. If you have not yet established accommodations through Accessibility Services but have a temporary health condition or permanent disability (conditions include but are not limited to: mental health, attention-related, learning, vision, hearing, physical or health), you are encouraged to contact Assistant Dean for Disability Services & Academic Success, Kari Rood, at AccessibilityServices@cmc.edu to ask questions and/or begin the process. General information and accommodations request information be found at the CMC DOS Accessibility Service’s website. Please note that arrangements must be made with advance notice in order to access the reasonable accommodations. You are able to request accommodations from CMC Accessibility Services at any point in the semester. Be mindful that this process may take some time to complete and accommodations are not retroactive. I would err on the side of caution and make sure your accommodations are sent to me even if you do not believe you need them as some students only learn they may need time after completing assessment. The Americans With Disabilities Act (ADA) and Section 504 of the Rehabilitation Act do not make accommodations retroactive. If you are approved for extra testing time for example, you must do so before an electronic assessment is posted in order for it to be integrated into the assessment. Claremont McKenna College values creating inclusive and accessible learning environments consistent with federal and state law. If you are not a CMC student, please connect with the Disability & Accessibility Services Coordinator on your campus regarding a similar process.

FYI on cheating etc.

Remember, you are responsible for not cheating or violating CMC’s Academic Integrity Policy. You are responsible for understanding that policy, and for conducting yourself in a manner such that you do not violate the policy.

The above link lists many examples of cheating and plagiarism that are not allowed. There are many more specific acts that you should NOT do. Here is an additional list of activities that will be sufficient cause for immediate failure in the course.

  • Do not take pictures of exam or quiz questions and share them with other students
  • Do not give other students answers during an exam or quiz, or any other assignment that is an individual assignment
  • Do not copy work from another source and submit it as your own
  • Do not copy and paste text from the internet and submit it as your own words
  • Do not copy and paste text and slightly alter wording to pass the work off as your own
  • Do not hire someone else to do the coursework for you
  • Do not copy and paste text into a paraphrasing app, and then submit the output of the paraphrasing app as your own work
  • Do not copy random words from the internet that have nothing to do with the assignment and submit them as your own work.
  • Do not work on individual assignments with other students, share answers or other material, and then all hand in versions of the same thing that are slightly different.
  • Do not plagiarize yourself by submitting work that you have previously completed in another class.

Mandate to report violations

If a faculty member suspects a violation of academic integrity and, upon investigation, confirms that violation, or if the student admits the violation, the faculty member MUST report the violation. Students should be aware that faculty may use plagiarism detection software.

There is no excuse for cheating. Students who are caught cheating may receive a failing grade for the entire course. All students found to have violated the academic integrity will be sanctioned by the Academic Standards Committee.

FAQ

If you have questions about the syllabus, let’s talk about it in class, and/or please create a thread to discuss the question on Discord.