Introduction to Computational Statistics for Data Scientists Specialization

This series of courses teaches the basics of Computational Statistics to help Data Scientists who are just starting out. This course is not meant to teach the fundamentals of statistics and probability. It does not cover Frequentist statistical techniques that are based on Null Hypothesis Significance Testing. It does include: Basics of Bayesian statistics, probability Understanding Bayesian Inference and How it Works The minimum set of tools and knowledge necessary to perform Bayesian Inference in Python. The PyData stack, which includes Pandas, Scipy and Matplotlib. It is a Python-based framework that allows for Bayesian inference. PyMC3 This content has been divided into three main sections (courses). Introduction to Bayesian Statistics: This course will introduce the attendees to the basics of Bayesian modeling, probability and inference. Introduction to Monte Carlo Methods. This will be followed up by lectures about how to infer approximate values when exact calculations are impossible in Course 2. PyMC3 for Bayesian Modeling, Inference and Bayesian Modeling - PyMC3 is introduced with some examples of its use in real-world situations. Lectures will be given through Jupyter notebooks. Attendees are encouraged to interact with these notebooks.