Data Visualization Learning Pod

Welcome to DareData's Data Visualization Learning Pod 🎉

This course is designed to teach how to improve your capabilities in narrating your data through visualization. In this introduction, you'll learn:

• The reason on why data visualization can have a large impact in anyone's professional life.
• The structure of the course.
• The expectations on students and mentors.
• A suggested learning calendar.

Learning Principles

When we assembled this course, we had in mind that our students would be adult individuals with time constraints. They will be looking to implement the knowledge in their work environment and maybe discuss it with their peers.

To fulfill these expectations, we adopted the following principles:

• Prefer self-directed learning over teacher-directed learning;
• Prefer content that's easily accessible (no paywalls or subscriptions);
• Prefer content that can be immediately applied;
• Always use visual examples;
• Learning by teaching is encouraged.

Learning Structure

Students are to be divided into groups of 3. These groups should try to progress through the learning process together so that their questions and discussion are on the same wavelength. Each group will be assigned a mentor and will perform code reviews together.

Mentors: A mentor is a more experienced collaborator and/or someone who has already gone through the course. They are in charge of helping their group, answering questions and preventing them from being stuck, as well as keeping track of their progress.

Challenges reviews: Some of the proposed assignments will involve reviewing each other's challenges. For each challenge, you'll have assigned a “reviewer”. It's the reviewer's responsibility to ensure that the challenge being reviewed fulfills the requirements of the assignment and is consistent on what is being asked to develop. During these assignments, each person will review the code of one of their group peers. The only caveat is that one can't review the code of the person who's reviewing their code.

The mentor will be available to offer help throughout every week, but only the reviewer can approve whether or not the assignment was completed.

Expectations

Expectations for students

Although we understand that time may be constrained, each student has responsibilities with its groups, namely:

• Try to keep the pace with the group's progress, neither falling too behind or advancing too much by themselves.
• Pay attention to the challenges reviews and perform changes accordingly.
• Don't ignore the questions and improvements asked by the person reviewing your code.
• Be courteous and respectful to your peers and mentor.
• Set your progress expectations with your mentor.
• Conduct yourself with integrity and honesty.

Expectations for mentors

Mentors are responsible for ensuring their peers become better professionals, as such, we expect them to:

• Reserve at least 30 minutes per week for each group you mentor, for answering questions and giving feedback.
• Encourage group members and communicate openly.
• Be courteous and respectful to your mentees.
• Ensure challenge reviews go smoothly: oversee and help, but don't overtake the reviewer's responsibilities.
• Keep track of questions and progress of the group members (see Progress tracking)
• Conduct yourself with integrity and honesty.

Progress and Questions Tracking

Progress tracking can happen in a variety of ways, for this course it's encouraged to be used a standard google sheet for each cohort, detailing advances for each student.

Mentors can/should track questions so that we can improve the quality of the selected material, as well as create new ones.

In order to help mentors in tracking the progress of their groups, you may find it useful to use this template:

Pre-requisites

In order to make the best use out of this learning path, you should know:

• Jupyter Notebook
• Work with an IDE (VS Code, PyCharm, Spyder, or other)
• Install a Business Intelligence software (Tableau Public Desktop or Power BI Desktop)
• Basic / Intermediary Python: some knowledge on EDA and data manipulation libraries such as pandas, matplotlib, seaborn and/or plotly and altair.
• Basic Git: add, commit, checkout, merge, and rebase.

Suggested learning calendar

Note: This is just a suggestion. Groups are encouraged to set their own deadlines with their mentors.

Week 01 (~2.5 hours)

• Intro to Visualization : Initial concepts and demand for visualization
• A tour through visualization techniques
• Visualization with matplotlib and seaborn

Week 02 (~2.5 hours)

• Visualization with Altair

Week 03 (~2.5 hours)

• EDA

Week 04 (~2.5 hours)

• Assignment #1

Week 05 (~2 hours)

• Intro to visualization software
• Datatypes, ERD, date tables and Calculation Fields/ DAX
• Filters, Hovers, Tooltips, Colouring, Maps

Week 06 (~2.5 hours)

• Assignment #2

Week 07 (~2 hours)

• Intro to Dashboarding
• Intro to Stories, multiple dashboards

Week 08 (~2 hours)

• Assignment #3

Week 09 (~1.5 hours)

• Publish and Service Configuration
• Presentation to stakeholders
• Impact of dashboards

Week 10 (~2.5 hours)

• Assignment #4

Assignments

Assignments are located inside the assignments folder.

In it, you'll find a project folder called california_housing as well as folders with instructions for each assignment. Each assignment builds upon the previous one, having some continuity throughout each stage.

Final expectations

With this course, we expect each student to be able to perform better analysis sustained on visualizations, wether it's during exporatory data analysis or when building data driven support dashboards made to be used by other stakeholders. We firmly believe that when you finish this course, you'll be equiped with another set of capabilities that will enhance your professional work , no matter your area of expertise.