Much of the popularity of Python stems from the availability of high quality libraries of existing code that we can use for our own projects. Libraries ("packages", in Python terminology) are even more useful when they are designed to work together.

For scientific programming, we are lucky to have a collection of mature packages which work together to form a stack:

numpy for numerical processing
pandas for reading, cleaning and processing tabular data files
matplotlib as a low-level charting library
seaborn as a high-level charting library for rapid dataset exploration through visualization
In this course we will learn how to use these packages together to quickly explore large biological datasets, find meaningful patterns in the data, and present our results clearly. We will focus on the high level packages - pandas and seaborn - as this will allow us to do the most work with the smallest amount of code. By concentrating on just two packages for an entire course, we will be able to cover a large part of what these tools can do.