This webinar takes place over two days on Tuesday January 16th and Wednesday January 17th.
Overview
Data visualization is a powerful medium for communication and a valuable partner in exploratory data analysis and statistical modelling. Over the course of two webinars, each an hour in length, you will be given a high-level introduction of how to connect data to different potential visualization designs and how to critically evaluate the resulting information. You will also be provided with practical examples of tools that you can use to visualize your own data. Overall, the goal of the webinar is to orient participants with the best practices from data visualization research and with various data visualization software tools.
Webinar materials including the presentation slide deck and training data used will be made available to all registrants.
Webinar objectives
By the end of this webinar, participants should be able to:
- Have a high-level understanding of data visualization creation and evaluation
- Have a basic understanding of different data visualizations tools as well as their strengths and limitations
Prior required knowledge
No prior knowledge of data visualization or data visualization software is expected. Familiarity with administrative healthcare data and basic statistical and data analysis procedures is assumed. Prior experience with R or Tableau is helpful but not essential.
In session 2 we will cover Tableau Public and Shiny data visualization software. Having these software tools available on your machine will help you follow along with the session, however, as it will be only a high-level overview it is not essential to have a downloaded version.
To download Tableau Public and Shiny (R) data visualization software please visit the following sites:
- Tableau Public: https://public.tableau.com/en-us/s/ (sign in, create a profile, download)
- Shiny: https://shiny.rstudio.com/ (requires R, download the shiny & tidyverse packages)
Webinar Content
Session 1: Data visualization in a nutshell
In this session, we’ll discuss data visualization through an analytic lens and draw parallels with basic statistical analysis. We will delineate the differences and similarities between graphic design and data visualization processes and discuss relevant data visualization design concepts from the computer science research community. Specifically, you will be taught how to breakdown a data visualization into tasks, data, and visual components, and how to use human centered design methodologies to guide visualization design and evaluation.
Session 2: Tableau, R, and all the others
This session will provide a high-level overview of the different data visualization tools out there and highlight their strengths and limitations. We’ll explore two tools in greater depth: Tableau Public and Shiny (an interactive data visualization package in R). You will be introduced to the differences between these two tools and have an opportunity to experience those differences through a basic walkthrough using the same dataset. This session is only meant to get you started with these tools and does not provide a thorough walk-through of either.
View recorded presentations below.
Instructor biography
Anamaria Crisan is a PhD candidate at the University of British Columbia studying the visualization of public health data under the joint supervision of Tamara Munzner (Computer Science) and Jennifer Gardy (School of Population and Public Health). She holds an MSc in bioinformatics and prior to her current studies, worked at a Vancouver-based prostate cancer genomics company and, more recently, at the British Columbia Centre for Disease Control supporting bioinformatic and statistical analysis of healthcare data.
