Regardless of the quality of your analyses and data-related findings, if you cannot effectively communicate them, their impact will be severely limited. Technical skills in this module will focus on a step-by-step walk through the process of choosing, creating, and modifying data visualizations in Python. Discussions will include general design principles applicable to other data visualization software used in industry and academia (eg. R, Tableau, PowerBI). Case studies and ‘real world’ examples are incorporated throughout. Ethics components include incorporating reproducibility with data visualization, building awareness of the decision making that goes into sharing data visually, and addressing inequity in data visualization by focusing on accessible design.

This course is designed for those who have a degree in something other than Computer Science/Statistics who are looking to enhance their data science and data visualization skills for their career.

By the end of this module, you will be able to:

1. Create and customize data visualizations start to finish in Python
2. Use general design principles for creating accessible and equitable data visualizations in Python and other software
3. Understand data visualization as purposeful/telling a story (and the ethical/professional implications thereof)

• Instructor: Ciara Zogheib (She/Her).
  • To contact me, please email me at [email protected], and include 'DSI Data Visualization' in the subject line!
  • I'll do my best to respond promptly, but please be patient!
• TA: Amanda Ng
  • To contact, please email [email protected], and include 'DSI' Data Visualization' in the subject line

The workshop will be held over two weeks, four days a week. Being mindful of online fatigue, there will be one break during each class where students are encouraged to stretch, grab a drink and snacks, or ask any additional questions.

Office hours with the TA will be held for half an hour before and after class on Mondays and Tuesdays (5:30-6:00 and 8:30-9:00), and for an hour after class on Wednesdays and Thursdays (8:30-9:30). Use office hours to discuss assignments with the TA or to ask about activities from class.

• Camera is optional although highly encouraged. I understand that not everyone may have the space at home to have the camera on.

• Monday 29 January, 6pm-8:30pm: Intro and overview, getting started with matplotlib
• Tuesday 30 January, 6pm-8:30pm: Exploring matplotlib, reproducible data visualization
• Wednesday 31 January, 6pm-8:30pm: Customizing our plots
• Thursday 1 February, 6pm-8:30pm: Choosing the right visualization
• Monday 5 February, 6pm-8:30pm: Subplots and combining visualizations
• Tuesday 6 February, 6pm-8:30pm: Accessible data visualization
• Wednesday 7 February, 6pm-8:30pm: Data visualization as advocacy, beyond matplotlib
• Thursday 8 February, 6pm-8:30pm: Industry guest speaker, course review

The course is a live-coding class. Students are expected to follow along with the coding. Students should be active participants while coding and are encouraged to ask questions throughout. Although slides will be available for students to reference, they should be referenced before or after class, as during class will be dedicated to coding with the instructor.

Below are the folders contained in this repo with a description of what they contain and information on how to use them.

This folder contains the assignments for the workshop.

This folder contains the pdf version of the slides. They contain all information that was discussed in class and are a great resource in the future if students need to reassess their knowledge.

• I wish to acknowledge this land on which the University of Toronto operates. For thousands of years it has been the traditional land of the Huron-Wendat, the Seneca, and most recently, the Mississaugas of the Credit River. Today, this meeting place is still the home to many Indigenous people from across Turtle Island and we are grateful to have the opportunity to work on this land.