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Visualization

Content

Description

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 of 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.

Learning Outcomes

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

  • Create and customize data visualizations from start to finish in Python
  • Apply general design principles to create accessible and equitable data visualizations
  • Use data visualization to tell a story

Assignments

  1. Assignment 1
  2. Assignment 2
  3. Assignment 3
  4. Assignment 4

Contacts

Questions can be submitted to the #questions channel on Slack

  • Technical Facilitator:
  • Learning Support Staff:

Delivery of the Learning Module

This module will include live learning sessions and optional, asynchronous work periods. During live learning sessions, the Technical Facilitator will introduce and explain key concepts and demonstrate core skills. Learning is facilitated during this time. Before and after each live learning session, the instructional team will be available for questions related to the core concepts of the module. Optional work periods are to be used to seek help from peers, the Learning Support team, and to work through the homework and assignments in the learning module, with access to live help. Content is not facilitated, but rather this time should be driven by participants. We encourage participants to come to these work periods with questions and problems to work through.   Participants are encouraged to engage actively during the learning module. They key to developing the core skills in each learning module is through practice. The more participants engage in coding along with the instructional team, and applying the skills in each module, the more likely it is that these skills will solidify.

Schedule

  • TBD: Intro and overview, getting started with matplotlib
  • TBD: Exploring matplotlib, reproducible data visualization
  • TBD: Customizing our plots
  • TBD: Choosing the right visualization
  • TBD: Subplots and combining visualizations + Accessible data visualization

Requirements

  • Participants are expected to have completed Shell, Git, and Python learning modules.
  • Participants are encouraged to ask questions, and collaborate with others to enhance their learning experience.
  • Participants must have a computer and an internet connection to participate in online activities.
  • Participants are expected to follow along with live coding.
  • Participants must not use generative AI such as ChatGPT to generate code in order to complete assignments. It should be used as a supportive tool to seek out answers to questions you may have.
  • We expect participants to have completed the steps in the onboarding repo.
  • We encourage participants to default to having their camera on at all times, and turning the camera off only as needed. This will greatly enhance the learning experience for all participants and provides real-time feedback for the instructional team.

Resources

Feel free to use the following as resources:

Documents

Videos

How to get help

image

Folder Structure

.
├── .github
├── 01_materials
├── 02_activities
├── 03_instructional_team
├── 04_cohort_three
├── .gitignore
├── LICENSE
├── README.md
└── steps_to_ask_for_help.png
  • .github: Contains issue templates and pull request templates for the repository.
  • materials: Module slides and interactive notebooks (.ipynb files) used during learning sessions.
  • activities: Contains graded assignments, exercises, and homework to practice concepts covered in the learning module.
  • instructional_team: Resources for the instructional team.
  • cohort_three: Additional materials and resources for cohort three.
  • .gitignore: Files to exclude from this folder, specified by the Technical Facilitator
  • LICENSE: The license for this repository.
  • README.md: This file.
  • steps_to_ask_for_help.png: Guide on how to ask for help.