Coursera - Bayesian Statistics Specialization 2022-5 - Download

Description

The Bayesian Statistics major is a Bayesian statistics training package for forecasting and modeling published by Coursera Academy. This training set will introduce you to the basics of Bayesian statistics and strengthen your data analysis skills. This educational package covers diverse and scattered topics such as statistics, Bayesian statistics, Bayesian inference, R programming language, etc. and is a complete and comprehensive educational collection. This set consists of four separate courses and a fully practical project, and during this training process various Bayesian statistical methods such as ex-conjugate distribution, Monte Carlo Markov chain, mixture models, dynamic linear modeling. And… you learn. All taught methods are used in professional statistical analysis, construction of various information models and statistical forecasting.

What you will learn in Bayesian statistics

Bayesian inference
Time series forecasting
Bayesian Hierarchical Modeling
Data science
R programming language
Information analysis and analysis
Bayesian statistics and statistics and its various methods
Gibbs sampling
Markov model
A mixture model
forecasting
dynamic linear modeling
time series

Course specifications

Publisher: Coursera
Supervisors: Herbert LeeMatthew Heiner, Abel Rodriguez, Raquel Prado, and Jizhou Kang
Language: English
Level: Medium
institution/university: University of California, Santa Cruz
Number of courses: 5
Duration: Approximately 6 months to complete - recommended pace 4 hours per week

Courses include:

Course 1

Bayesian Statistics: From Concept to Data Analysis

2nd course

Bayesian Statistics: Techniques and Models

3rd course

Bayesian statistics: mixture models

4th course

Bayesian Statistics: Time Series Analysis

5th course

Bayesian Statistics: A Capstone Project

Bayesian Statistics Major Prerequisites

What background knowledge is required?

previous work experience with calculations (you don't need to remember how to do it, it's enough to understand the concepts); introductory statistics course.