Visualization

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 walkthrough of choosing, creating and modifying data visualizations in R using ggplot. Discussions will include general design principles applicable to other data visualization software used in industry and academia (e.g., Python, Tableau, PowerBI).

Case studies and real-world examples will be 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.

Requirements

This course is designed for those who have a degree in something other than Computer Science/Statistics and are looking to enhance their data science skills for their career.

Learning Outcomes

  • The ability to create and customize data visualizations start to finish in R
  • An understanding of general design principles for creating accessible/equitable data visualizations in R and other software
  • An understanding of data visualization as purposeful/telling a story (and the ethical/professional implications)

Delivery Format & Schedule

Online for 7 hours/week for 3 weeks (21 hours in total).

  • Monday 13 March, 6pm-8pm: Overview and introduction
  • Thursday 16 March, 6pm-8pm: Setting up data in R and making preliminary ggplots
  • Saturday 18 March, 9am-noon: Graphing the data through transforming, faceting, and grouping
  • Monday 20 March, 6pm-8pm: Visualization with purpose: apply themes
  • Thursday 23 March, 6pm-8pm: Understanding incorporate elements of accessible design
  • Saturday 25 March, 9am-noon: Getting fancy: making interactive and reproducible plots
  • Monday 27 March, 6pm-8pm: Reproducibility; ethics; inequity
  • Thursday 30 March, 6pm-8pm: Professional skills: Industry case study
  • Saturday 1 April, 9am-noon: Visualization: Review and Practice