DATA / STAT 334

Syllabus and Course Information

General Information

Instructor Information

  • Professor: Matt Higham
  • Office: Bewkes 123
  • Email: mhigham@stlawu.edu
  • Office Hours:
  • Sections:
    • MW 8:50 - 10:20

Course Materials

Course Information

Welcome to DATA/STAT 334: Data Visualization! In this course, we will expand upon DATA/STAT 234 and STAT 213 to focus more on visualizing data and visualizing models. In the 9-10 weeks of the course, we will build a foundation for a more broad, conceptual understanding of data viz and its usefulness. In the final 4-5 weeks of the course, you will focus in-depth on visualizing one or more data sets in an extensive project.

General Course Outcomes

  1. Explain why it is useful to visualize data in a variety of contexts.

  2. Explain what makes a data graphic “good” and what makes a data graphic “poor.”

  3. Discuss the ethics of various data visualizations.

  4. Use R and R Studio to construct various data visualizations for a variety of data sets.

  5. Use visualization to convey meaning the meaning of regression models to a general audience.

  6. Use Git and GitHub for version control and as a way to easily share with others your code and project.

  7. Create a larger-scale final project that showcases an in-depth visual analysis of one or more data sets.

Use of RStudio and GitHub

We will use the statistical software R and the R Studio IDE throughout the course for a few reasons:

  • R and RStudio are both free to use.
  • One pre-requisite for this course is DATA/STAT 234, meaning that everyone in this course has some background already with using R.
  • R Studio has a nice Git interface.

In addition to R Studio, we will use Git and GitHub for version control. GitHub will also be the location where you can obtain materials from missing class link to GitHub repository . We will discuss what these are and why they are useful more in class.

How You Will Be Assessed

There are 1000 total points that can be earned in this course. The primary purpose of the first two-thirds of the course is to give you breadth of knowledge in the topic of data visualization while the primary purpose for the last third of the course is to give you an opportunity to work on a visualization project of interest in sufficient depth. The specific components that you will be assessed on are described next.


Exercises and Participation

There will be sets of “Exercises”, each worth between 5 and 10 points for a total of 200 points. What these points will come from include:

  • class prep materials that consist of completing some of our course materials (in the sections called “Class Prep”) before class.
  • evaluation of your participation in class a few times throughout the semester
  • Feedback rubrics on final projects for other students in the class and/or GitHub pushes on your own final project (completed in the back third of semester).

Collaboration is permitted on all exercise sets.

Blog

During the first part of the semester, you will write 3 blog posts on a site that you will construct with Quarto and publish this blog through GitHub pages. Each blog post is worth 30 points, and there will be an additional 10 points associated with the third blog post for a total of 100 points.

Assessments

Throughout the semester, there will be 5 assessments, each worth 100 points. Each assessment will consist of

  • a take-home component (worth approximately 10 points)
  • a handwritten in-class component (worth anywhere from 10 to 80 points).
  • a coding in-class component (worth anywhere from 10 to 80 points).

Final Project

Finally, there are 200 points for the final project. These points will be split amongst a few different components of the final project: details will be provided later in the semester.

Point Breakdown

Therefore, based on the information above, the point breakdown for the course is:

  • 200 points for exercises, participation, GitHub pushes, and feedback rubrics.
  • 100 points for blog posts.
  • 500 points for assessments.
  • 200 points for the final project.

Points add up to 1000 so your grade at the end of the semester will be the number of points you’ve earned across all categories divided by 1000.

Grading Scale

The following is a rough grading scale. I reserve the right to make any changes to the scale if necessary.

Grade 4.0 3.75 3.5 3.25 3.0 2.75 2.5 2.25 2.0 1.75 1.5 1.25 1.0 0.0
Points 950-1000 920-949 890-919 860-889 830-859 810-829 770-809 750-769 720-749 700-719 670-699 640-669 600-639 0-599

Collaboration, Diversity, Accessibility, and Academic Integrity

Rules for Collaboration

Collaboration with your classmates on the exercises is encouraged, but you must follow these guidelines:

  • you must state the name(s) of who you collaborated with at the top of each exercise set.
  • all work must be your own. This means that you should never send someone your code via email or let someone directly type code off of your screen. Instead, you can talk about strategies for solving problems and help or ask someone about a coding error.
  • you may use the Internet and StackExchange, but you also should not copy paste code directly from the website, without citing that you did so.
  • use of generative AI (like Chat GPT) is not permitted on any of the assessments at all. If you use AI for help on something in your blog post or final project, you should include the search prompt and what you are using.

Diversity Statement

Diversity encompasses differences in age, colour, ethnicity, national origin, gender, physical or mental ability, religion, socioeconomic background, veteran status, sexual orientation, and marginalized groups. The interaction of different human characteristics brings about a positive learning environment. Diversity is both respected and valued in this classroom.

Accessibility Statement

Your experience in this class is important to me. It is the policy and practice of St. Lawrence University to create inclusive and accessible learning environments consistent with federal and state law. If you have already established accommodations with the Student Accessibility Services Office, please meet with them to activate your accommodations so we can discuss how they will be implemented in this course. If you have not yet established services through the Student Accessibility Services Office but have a temporary health condition or permanent disability that requires accommodations (conditions include but are not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), please contact the Student Accessibility Services Office directly to set up a meeting to discuss establishing with their office. The Student Accessibility Services Office will work with you on the interactive process that establishes reasonable accommodations.

Color-Vision Deficiency: If you are Color-Vision Deficient, the Student Accessibility Services office has on loan glasses for students who are color vision deficient. Please contact the office to make an appointment.

For more specific information about setting up an appointment with Student Accessibility Services please see the listed options below:

  • Telephone: 315.229.5537
  • Email: studentaccessibility@stlawu.edu

For further information about Student Accessibility Services you can check the website at: https://www.stlawu.edu/student-accessibility-services

Academic Dishonesty

Academic dishonesty will not be tolerated. Any specific policies for this course are supplementary to the

Honor Code. According to the St. Lawrence University Academic Honor Policy,

  1. It is assumed that all work is done by the student unless the instructor/mentor/employer gives specific permission for collaboration.
  2. Cheating on examinations and tests consists of knowingly giving or using or attempting to use unauthorized assistance during examinations or tests.
  3. Dishonesty in work outside of examinations and tests consists of handing in or presenting as original work which is not original, where originality is required.

Claims of ignorance and academic or personal pressure are unacceptable as excuses for academic dishonesty. Students must learn what constitutes one's own work and how the work of others must be acknowledged.

For more information, refer to www.stlawu.edu/acadaffairs/academic_honor_policy.pdf.

To avoid academic dishonesty, it is important that you follow all directions and collaboration rules and ask for clarification if you have any questions about what is acceptable for a particular assignment or exam. If I suspect academic dishonesty, a score of zero will be given for the entire assignment in which the academic dishonesty occurred for all individuals involved and Academic Honor Council will be notified. If a pattern of academic dishonesty is found to have occurred, a grade of 0.0 for the entire course can be given.

Please note that in addition the above, any assignments in which your score is reduced due to academic dishonesty will not be dropped according to the quiz policy e.g., if you receive a zero on a quiz because of academic dishonesty, it will not be dropped from your grade.

Tentative Schedule

Week Date Topics
0 1/17 Core Concepts
1 1/22 Core Concepts and Introduction to Git and GitHub
2 1/29 DATA/STAT 234 Review and Assessment 1
3 2/5 Mapping and Expressing Uncertainty and Blog Post 1
4 2/12 STAT 213 Review and Assessment 2
5 2/19 Model Visualization and Blog Post 2
6 2/26 Logistic Regression and Assessment 3
7 3/4 Other Topics, Ethics, and Blog Post 3
8 3/11 Interactivity, Project Introduction, and Assessment 4
3/18 Spring Break
9 3/25 Shiny Introduction
10 4/2 Shiny Reactivity and Assessment 5
11 4/9 Project
12 4/16 Project
13 4/23 Project
14 4/30 Project
  • There will be no Final Exam, but keep your schedule open at our Final Exam time in case we decide to use it for something.