Data Visualization

This course can be applied toward the UBC Certificate in Key Capabilities in Data Science. You must complete Programming in Python for Data Science before starting this course.

This course focuses on practical tech skills, with emphasis on transferable knowledge and a critical thinking approach for immediate application in your current work. 

Understand effective data visualizations, perform exploratory data analysis using Altair in Python and learn how to describe your findings.   

By the end of this course, you’ll be able to: 

• Use the Altair grammar of graphics to create data visualizations 
• Select an appropriate visualization for the data 
• Perform exploratory data analysis on a dataset 
• Effectively communicate findings with figures 
• Interpret data visualizations to answer questions and formulate follow-up questions

Basic knowledge of Python and working with data is required. This course uses Altair in Python and is designed for learners with some background in programming.

Week 1 and Week 2 

• Module 1: Why visualize data, grammar of graphics, introduction to Altair
• Module 2: Effective use of visual channels and customizing plot axes, visualize frequencies, and create faceted plots for comparing groups
• Module 3: Visualizing distributions: density plots, histograms, boxplots and violin plots 

Week 3 and Week 4 

• Module 4: Visualizing 2D distributions with heatplots, visualizing correlations and counts, and creating repeat plots for exploring relationships in the dataset 
• Module 5: Designing and optimizing plots for communication 

Week 5 

• Quiz 1 
• Final project: Part 1 of 2­—Exploratory data visualization document  

Week 6 and Week 7 

• Module 6: Narrative, figure composition and geographical visualizations 
• Module 7: Interactive visualizations 

Week 8 

• Quiz 2 
• Final project: Part 2 of 2

How am I assessed?

Each course module includes an auto-graded assignment. In weeks 5 and 8, you take an online 45-minute open-book quiz that covers materials from modules 1–4 and 5–7 respectively. In Week 8, you complete a final project using the skills you learned in the course. You must obtain an overall grade of 70% or higher, and complete the final project, to pass the course.

Expected effort

Expect to spend 8–12 hours per week to complete weekly modules, auto-graded quizzes, open-book quizzes and the final project.

Technology Requirements

To take this course, and for the best experience, we recommend you have access to:

• an email account
• a computer, laptop or tablet
• the latest version of a web browser (or previous major version release)
• a reliable internet connection.

For virtual office hours, you’ll also need:

• a video camera and microphone.

One day before the start of your course, we’ll email you step-by-step instructions for accessing your course.

Requisites

The prerequisite course is Programming in Python for Data Science.

You must complete the prerequisite course before starting Data Visualization.

Course format

This course is 100% online and facilitator supported with weekly facilitator office hours. Course work is done independently and at your own pace within deadlines set by your facilitator. Log in anytime to your course to access the modules.

Course virtual office hours (subject to change)

• Mondays, 6-7pm Pacific Time 
• Saturdays, 10-11am Pacific Time

Join your facilitator and classmates by video conferencing to discuss course materials and assignments, receive feedback and ask questions.