Effective visual communication (EVC) is a core competency for all scientists who work with data, such as statisticians, pharmacometricians, epidemiologists, data scientists, etc. By using the right graphical principles, we can better understand data, highlight core insights, and influence decisions toward appropriate actions. Without them, we can mislead others and ourselves and pave the way to wrong conclusions and actions.
The aim of this course is to provide a deep dive into EVC for quantitative scientists through a series of case studies and hands-on exercises, focusing on the key principles of Purpose, Clarity and Message. Illustrative examples will be drawn from medical research, but the course is designed to be general for all quantitative experts communicating data, analyses, and conclusions. The course will help put the three EVC principles into practice through active learning, with the tutors acting as facilitators. Hands-on exercises will allow participants to work on problems relevant to their own projects. The course will be hands-on and fun, with lots of drawing and interaction.
The course requires no prior knowledge. We will recommend the participants (but not mandatory) to
• Watch the 4 minute EVC video
• Become familiar with the Graphics Principles Cheat sheet
• Read the EVC Tutorial
All available here: https://graphicsprinciples.github.io/
Also recommended is to select (and ideally print) a graph from one of their current or previous projects that they will want to work on improving during the hands-on sessions.Learning Objectives
• Appreciate why effective visual communication (EVC) is a key competency for the quantitative scientist.
• Explain the three principles of EVC (purpose, clarity and message).
• Design a visualization based on a specific purpose.
• Redesign a visualization to show data clearly.
• Enhance the message of a visualization.
• Recognize where to apply the three principles of effective visual communication in your daily work.Equipment Required
Participants should bring colored pencils & paper.About the Instructors
Mark Baille is a Director Data Scientist in the Advanced Methodology and Data Science group at Novartis. He is a methodologist supporting the clinical development and analytics department at Novartis. He has a focus on data visualization working on a number of internal and external initiatives to improve the reporting of clinical trials and observational studies.
Marc Vandemeulebroecke is a Global Group Head in Novartis’ Analytics department. He holds a PhD in mathematical statistics from the University of Magdeburg and an MSc in PKPD modeling & simulation from the University of Manchester. He received the Gustav-Adolf-Lienert award from the German Region of the International Biometric Society (IBS). Marc’s interests range from adaptive clinical trials to effective statistical graphics, statistical and machine learning, and developing effective teams of statisticians.
Mark and Marc are part of a team engaged in improving the practice of quantitative graphics through sharing and teaching. We have presented with RStudio and taught a short course with ASCPT (similar to the one we propose for IBC), and we are co-leading various related efforts internally at Novartis.