In these lessons, we will learn about foundational concepts in probability and statistics that will help us build an understanding of what we need to perform to test our hypothesis using data. The first session (October 8th) will be dedicated to descriptive statistics and visualisation. We will review basic metrics and distributions, how to visualise datasets using Matplotlib and Seaborn, and how to summarize and describe our data with key metrics. The second session (October 22nd) will focus on inferential statistics: how can we measure the presence of effect, patterns, and relationships between variables while taking uncertainty into account? The exercises will be provided as Jupyter notebooks and will utilize Python libraries such as NumPy, SciPy, Pandas, Pingouin, and Seaborn.
Nicolas Legrand & Sylvain Estebe