Introduction to Bayesian Statistics

Databricks via Coursera

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Overview

This course provides a thorough introduction to Bayesian statistics, a powerful approach to data analysis that incorporates prior knowledge with observed data to update beliefs. You will learn fundamental concepts including Bayes' theorem, prior and posterior distributions, likelihood functions, and Bayesian inference. The course covers practical applications in various fields such as machine learning, epidemiology, and finance. You will explore computational techniques like Markov Chain Monte Carlo (MCMC) methods to perform Bayesian analysis in real-world scenarios. Emphasis is placed on...

Syllabus

Environment Setup

Introduction to the compute environment for the Specialization. The users will be introduced to the Databricks Ecosystem for Data Science. The users can also deploy the notebooks to Binder for setup-free access.

Introduction to the Fundamentals of Probability

In this module, you will learn the foundations of probability and statistics. The focus is on gaining familiarity with terms and concepts.

A Hands-On Introduction to Common Distributions

Tis module will be an introduction to common distributions along with the Python code to generate, plot and interact with these distributions. You will also learn how to perform Maximum Likelihood Estimation (MLE) for various distributions and Kernel Density Estimation (KDE) for non-parametric distributions.

Sampling Algorithms

This module introduces you to various sampling algorithms for generating distributions. You will also be introduced to Python code that performs sampling.