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Data Visualization

Syllabus

This is the syllabus for the Data Visualization (STAT 2430) course in Winter 2021 at Dalhousie University.

Course Description

Data visualization is the art and science of turning data into readable graphics. We will explore how to design and create data visualizations. This exploration will include both the principles and techniques of data visualization. Students will learn the value of visualization, specific techniques and understand how to select the best visualization method.

Course Prerequisites

At least one MATH or STAT course at or above the 1000 level. No experience with R or computer programming is required, but a desire to learn about both is essential.

Course materials

Course notes:

Required textbook:

Supplemental textbooks:

The textbook and supplemental textbooks are available in printed form and online. The online versions are free to use. You are not required to buy the books on paper. All three are excellent books with very distinct goals.

There are a lot of materials for this course. The course videos and notes highlight the most important information, with many references to the textbooks and other online materials.

Course structure

Each week will be structured around the following components:

  • Watching recorded mini-lectures for each lesson
  • Reading course notes and excerpts from the textbooks
  • Scheduled office hours (Tuesday 8:30 am Atlantic). Come if you have questions or want to listen in to discussions about course material based on questions asked by other students.
  • Live coding tutorial / lab (Thursday 8:30 am Atlantic)
  • Opportunities to develop and demonstrate your knowledge (tasks, assignments, and a term project)

Follow the detailed outline for each week’s plan to keep you on track.

If you have questions about the course material, please ask them during the synchronous meetings or office hours. Between meetings, questions should be asked on the Piazza forum. If you have questions specific to your situation that should not be shared with the class, please send me an email. Please only use email for communication that should be private, as general questions are best asked in public where everyone in the class can benefit from the exchange. Please use the discussion forum to talk with other classmates and help each other.

We will use Collaborate Ultra accessed through Brightspace for the interactive sessions, which will help structure your work. These will usually be recorded so that you can watch them later if you are unable to participate. You are strongly encouraged to come to these meetings since the interactive sessions are the primary forum for asking questions and discussing course material. I strongly encourage you to “raise your hand” during the discussion and ask questions using your voice. Some students prefer to use the chat to ask questions, and that is fine with me, but from my perspective it leads to a more disjointed and less useful interaction.

Plan to review the agenda for each meeting before it begins.

Evaluation

Tasks are opportunities to test your understanding of the key concepts from the lessons and demonstrate you have developed basic proficiency with essential computing skills. Most lessons will have a task for you to complete.

Assignments combine skills from multiple lessons into a meaningful data visualization activity. These are opportunities to apply the content of lessons in thoughtful and sometimes creative ways.

The final project is an opportunity to combine many of the skills learned in the course. You will explore, analyze, and present to a reader an analysis of a dataset of your choice. Your proposal will select a dataset and describe some of your planned analyses. The oral presentation will be a 5 minute overview of your data and key visualizations. The report will be a polished reproducible document demonstrating many of the ideas of the course using your selected dataset.

  • Tasks for most lessons (30%, roughly 2 per week, due weekly, equally weighted)
  • Assignments (40%, roughly 1 every two weeks, equally weighted)
  • Term project, divided into three components
    • Proposal, due Friday 12 March, 5%
    • Oral presentation, due Thursday 1 April, 10%
    • Report, due Friday 9 April, 15%

Tasks are due on Monday. Assignments are due on Wednesday. Late work will be accepted without penalty until the work is graded or solutions are posted. After that, no later work will be accepted, but your two lowest (or missing) task marks will be dropped from your evaluation. If an additional accommodation is requested and granted, the value of that work will be redistributed to other tasks or assignments. If you anticipate not being able to submit the proposal, oral presentation, or report on time, please contact me by email.

Grades will be reported on Brightspace

Software

We will use the statistical software R and RStudio and version control software git. No prior experience with R, RStudio or git is assumed. We’ll take class time to learn the software.

These tools can be installed on Linux computers as well. Contact me if you have trouble. If you have a Chromebook you can use all these tools through the cloud service rstudio.cloud. Everyone should learn to use the cloud service as a backup in case of problems with setting up the software on their own computer.

R and Rstudio are available on Dalhousie computer labs, but the git version control software must be installed following the instructions for Windows computers above. Since all your user files are erased from lab computers when you log out, this process must be repeated on each login.

Videos demonstrating how to install this software are on Brightspace.

Course Policies

Credit will not be given for assignments submitted after grading is complete or the solutions are posted. If you miss an assignment due to illness or other event outside your control, contact me for an accommodation. If an accomodation is granted, assignment weight will be shifted to the rest of the assignments. Your two lowest Task grades will be dropped from the calculation of your final grade automatically and as a result I will be reluctant to offer accommodation for late or missed tasks.

Your goal in this course is to learn the principles and skills of data visualization. Most people benefit from a mixture of collaborative and independent work. The general guideline is that work you submit should be your own—your ideas, your thoughts, your choices, your code, your typing. You are encouraged to discuss your work with the instructor and students. When you help another student, be careful you don’t help them so much that you inhibit their learning.

Tasks are primarily designed to help you learn and you are encouraged to seek assistance from classmates, but work you submit must be your own. Assignments are assessments of your skills and should be done independently. The final project and its components may be done in groups, but the work on the project should only be the work of members of the group.

Copying work from any other sources is not allowed and will be considered an academic offense for this course. You are encouraged to learn from many different sources. If you make use of material outside of course materials on assignments or the project report, include references to the material and a description of what you used in a “Sources used” section at the end of your work.

Important dates

  • Wed 6 Jan: Term begins
  • Fri 5 Feb: Munro day, University closed
  • 15-19 Feb: Study break
  • Fri 12 March: Project proposal due
  • Fri 2 Apr: Good Friday, University closed
  • Tue 6 Apr: Last Tuesday/Thursday class
  • Thu 8 Apr: Last day of classes
  • Fri 9 Apr: Final project due
  • 10-23 Apr: Exam period

Letter grades

Your numerical grade will be converted to a letter grade for the course using the Dalhousie Common Grade Scale.

First your numerical grade will be rounded up to the nearest integer, then it will be converted to a letter using this table.

Letter grade Grade range
A+ 90-100
A 85-89
A- 80-84
B+ 77-79
B 73-76
B- 70-72
C+ 65-69
C 60-64
C- 55-59
D 50-54
F 0-49

University Policies and Statements

This course is governed by the academic rules and regulations set forth in the University Calendar and by Senate

Missed or Late Academic Requirements due to Student Absence. As per Senate decision instructors may not require medical notes of students who must miss an academic requirement, including the final exam, for courses offered during fall or winter 2020-21 (until April 30, 2021). Information on regular policy, including the use of the Student Declaration of Absence can be found here.

Academic Integrity. At Dalhousie University, we are guided in all of our work by the values of academic integrity: honesty, trust, fairness, responsibility and respect (The Center for Academic Integrity, Duke University, 1999). As a student, you are required to demonstrate these values in all of the work you do. The University provides policies and procedures that every member of the university community is required to follow to ensure academic integrity. More information.

Accessibility. The Advising and Access Services Centre is Dalhousie’s centre of expertise for student accessibility and accommodation. The advising team works with students who request accommodation as a result of a disability, religious obligation, or any barrier related to any other characteristic protected under Human Rights legislation (Canada and Nova Scotia). More information.

Student Code of Conduct. Everyone at Dalhousie is expected to treat others with dignity and respect. The Code of Student Conduct allows Dalhousie to take disciplinary action if students don’t follow this community expectation. When appropriate, violations of the code can be resolved in a reasonable and informal manner—perhaps through a restorative justice process. If an informal resolution can’t be reached, or would be inappropriate, procedures exist for formal dispute resolution. Code.

Diversity and Inclusion - Culture of Respect. Every person at Dalhousie has a right to be respected and safe. We believe inclusiveness is fundamental to education. We stand for equality. Dalhousie is strengthened in our diversity. We are a respectful and inclusive community. We are committed to being a place where everyone feels welcome and supported, which is why our Strategic Direction prioritizes fostering a culture of diversity and inclusiveness. Statement.

Recognition of Mi’kmaq Territory. Dalhousie University would like to acknowledge that the University is on Traditional Mi’kmaq Territory. The Elders in Residence program provides students with access to First Nations elders for guidance, counsel and support. Visit or e-mail the Indigenous Student Centre (1321 Edward St) (). Information.