Scientific data visualization

This course aims to teach data visualization and its concepts. Focus will be on hands-on exercises across multiple real cases in different scenarios. We aim to take practitioners through the myriad set of activities that will help create impact visualizations. We will cover concepts, tools, techniques as well as demystify the various concepts associated with data visualization and the various packages.
We'll also be looking at how data visualization can help in presence of mistakes, biases, systematic errors, outliers and other unexpected problems often lead to data that should be handled with care.

At the end of the course students will know the scientific data visualization principles, how to communicate data-driven findings, how to use R and Python (and other commercial software) to create custom plots, the weaknesses of several widely-used plots and why you should avoid them, how to identify 'problems' in the data.