The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:nnThe basics of Bayesian statistics and probabilitynnUnderstanding Bayesian inference and how it worksnnThe bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot.lynnA scalable Python-based framework for performing Bayesian inference, i.
e. PyMC3nnWith this goal in mind, the content is divided into the following three main sections (courses).nnIntroduction to Bayesian Statistics – The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1.nnIntroduction to Monte Carlo Methods – This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2.nnPyMC3 for Bayesian Modeling and Inference – PyMC3 will be introduced along with its application to some real world scenarios.nnThe lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks.
