Computation Statistics

Learn Fundamentals of Statistics with supporting case studies

Learn Bayesian Inference, Probability Distributions, MCMC Methods, and Statistical Modeling Step by Step with Examples

Apply Statistics in Real World Business Problems Using Python. Build a Career in Data Science and Business Analytics.

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.

16 Hours Course especially designed for the University Students who want to become Expert from very Basics Level.