Introduction to Computational Statistics for Data Scientists

Introduction to Computational Statistics for Data Scientists is a comprehensive series of courses designed to teach the basics of computational statistics, specifically focusing on Bayesian inference for aspiring or new Data Scientists. The course content is divided into three main sections, each covering essential topics and tools required for performing Bayesian inference in Python.

The first section, Introduction to Bayesian Statistics, introduces the basics of probability, Bayesian modeling, and inference. Participants will gain hands-on experience using Python for computational statistics through the utilization of Scikit-learn, SciPy, and Numpy.

The second section, Bayesian Inference with MCMC, delves into Markov Chain Monte Carlo algorithms and their implementation in Python. Participants will learn how to assess the performance of Bayesian models, especially in scenarios where exact calculations are not feasible.

The final section, Introduction to PyMC3 for Bayesian Modeling and Inference, focuses on the PyMC3/ArViz framework for Bayesian modeling and inference. Participants will have the opportunity to build real-world models using PyMC3 and evaluate the quality of their models.