# Bayesian Statistics: A Step-by-step Introduction

A former Google data scientist helps you master the basics of Bayesian statistics, with examples in R and Stan

## Enroll Now

### What you'll learn

• Understand how Bayes' rule can be used to update beliefs
• Use conjugate priors and likelihoods to model binary, count, and continuous data
• Understand the concepts of prior distributions, posterior distributions, likelihood functions, and predictive distributions
• Understand how statistical software can be used to compute and visualize information about your beliefs

### Requirements

• Strong skills in basic algebra and arithmetic
• Some knowledge of calculus is useful, but not required.

This course teaches the foundational material of statistics covered in an introductory college course, with a focus on mastering the basic components of any Bayesian model - the prior distribution and the likelihood, and how to find a posterior distribution, credible intervals, and predictive distributions.  Along the way, you'll become more comfortable with probability in general and gain a new perspective on how to analyze data!

We start from scratch - no experience in Bayesian statistics is required.  Students should have a strong grasp of basic algebra and arithmetic.  R and RStudio, or Python, is required if you would like to run the optional coding sections

### The course includes:

• 5.5 hours of video lectures
• Interactive demonstrations using R and Stan (Python code is included too!)
• Quizzes to check your understanding
• Review assignments with solutions to practice what you have learned

### You will learn:

• The basic rules of probability
• Bayes' rule, including common examples with medical testing and flipping coins
• The terminology of different components of a Bayesian model: the prior distribution, posterior, likelihood, and predictive distribution
• Conjugate priors
• Credible intervals and Bayes estimators
• Modeling binary data with the Bernoulli and Binomial Distribution, and the Beta distribution prior
• Modeling count data with the Poisson Distribution, and the Gamma distribution prior
• Modeling continuous data with the Normal Distribution, and the Normal distribution prior
• An introduction to simple linear regression

### This course is ideal for many types of students:

• Anyone who wants to learn the foundations of Bayesian statistics and understand concepts like priors, posteriors and credible intervals
• Data science and data analytics professionals who would like to refresh and expand their statistics knowledge
• Academics in the social, biological, and physical sciences
This course is ideal for anyone, from beginners to seasoned professionals. It doesn't matter if you're just starting your journey in data science, looking to upgrade your existing skills, or simply have an interest in Bayesian statistics. My goal is to make Bayesian statistics accessible and understandable for all.

### Who this course is for:

• Current and aspiring data scientists and data analysts
• Academics in the social, biological, and physical sciences
• University students studying mathematics or statistics
• Anybody who wants to learn to rigorously update their beliefs from data.
Online Course CoupoNED based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.