- Description
- Learning Outcomes
- Assignments
- Contacts
- Delivery of the Learning Module
- Schedule
- Requirements
- Resources
- Folder Structure
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.
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
Questions can be submitted to the #questions channel on Slack
- Technical Facilitator:
- name and pronouns:
Eshta,She/Her - email:
eshta.bhardwaj@mail.utoronto.ca - (Please reach out over email rather than Slack, if possible.)
- name and pronouns:
- Learning Support Staff:
- name:
Fan - email:
fw2400@cumc.columbia.edu - name and pronouns:
Kasra, - email:
k.vakiloroayaei@utoronto.ca
- name:
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.
- Class 1: Intro and overview (slide 01), Getting started with matplotlib (slide 02)
- Class 2: Choosing the right visualization (slide 04)
- Class 3: Reproducible data visualization (slide 03), Customizing our plots (slide 05)
- Class 4: Subplots and combining visualizations (slide 06), Accessible data visualization (slide 07)
- Class 5: Data viz as advocacy (slide 08), Beyond matplotlib (slide 09)
- 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.
Feel free to use the following as resources:
- Copy and paste your error message
- Copy and paste the code that caused the error, and the last few commands leading up to the error
- Write down what you are trying to accomplish with your code. Include both the specific action, and the bigger picture and context
- (optional) Take a screenshot of your entire workspace
- Sometimes, the error has common solutions that can be easy to find!
- This will be faster than waiting for an answer
- If none of the solutions apply, consider asking a Generative AI tool
- Paste your code, the error message, and a description of your overall goals
- Since we're all working through the same material, there's a good chance one of your peers has encountered the same error, or has already solved it
- Try searching in the DSI Certificates Slack help channel for whether a similar query has been posted
- If the question has not yet been answered, post your question!
- Describe your the overall goals, the context, and the specific details of what you were trying to accomplish
- Make sure to copy and paste your code, your error message
- Copying and pasting helps:
- your peers and teaching team quickly try out your code
- others to find your question in the future
.
├── .github
├── 01_materials
├── 02_activities
├── 03_instructional_team
├── 04_cohort_three
├── .gitignore
├── LICENSE
└── README.md- .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.