Introduction to Bayesian Statistics (Coursera)

What You Will Learn

- The basics of Probability, Bayesian statistics, modeling and inference.

- You will also get a hands-on introduction to using Python for computational statistics using Scikit-learn, SciPy and Numpy.

Course 1 of 3 in the Introduction to Computational Statistics for Data Scientists Specialization

Syllabus

WEEK 1

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.

WEEK 2

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.

WEEK 3

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.

WEEK 4

Sampling Algorithms

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