Scientific Data Presentation

Course Description

The goal of the course is to give PhD students the ability to effectively present the data resulting from their experiments. This is a very important form of communication for any researcher, just as important as writing and speaking.

All researchers at our faculty deal with numeric data on a day to day basis. We first plot this data to understand it ourselves, then use those same graphs in our presentations, posters and journal articles. However, this is not necessarily the best way to present that data. Poor graphs are published all the time: graphs that need lots of explanations before they are understandable, that obscure the relevant information, that waste valuable space... With a little thought and some knowledge of tools, graphs can be effective, efficient and beautiful.

The course will cover different forms of graphs and tables for one and two- dimensional sampled data, categorical data, discrete values, etc.; certain aspects of human perception relevant to displaying data, including colour perception; the need to highlight the story in the data, refraining from displaying the non-essential things (without, of course, misrepresenting the data); and how to use drawing tools such as Illustrator or Inkscape to edit figures generated by Excel, MATLAB, or any other graphing tool.

The course will have some lectures and demos. Additionally, many real-world examples will be dissected and discussed. By redesigning published graphs, the student will get hands-on experience with the iterative process of designing effective data displays.

To receive credit, students are expected to attend all lectures and discussion sessions, and complete the homework exercises. Homework exercises will be the basis of some of the discussion sessions.

Goals

After the course, the student shall:

have experience analyzing and critiquing graphical displays of data;
have a good understanding of the existing types of data display, and when to use which one;
have knowledge about some aspects of human visual perception, as it relates to choosing ways to display data;
know all the common pitfalls and how to avoid them; and
have some experience with editing figures in a drawing program such as Inkscape.