40,396

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 30 hours to complete

Suggested: 5 weeks of study, 5-7 hours/week...

#### English

Subtitles: English

### Skills you will gain

Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 30 hours to complete

Suggested: 5 weeks of study, 5-7 hours/week...

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
1 hour to complete

## About the Specialization and the Course

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Bayesian Statistics. Please take several minutes read this information. Thanks for joining us in this course!...
1 video (Total 2 min), 4 readings
1 video
Pre-requisite Knowledge10m
Special Thanks2m
6 hours to complete

## The Basics of Bayesian Statistics

<p>Welcome! Over the next several weeks, we will together explore Bayesian statistics. <p>In this module, we will work with conditional probabilities, which is the probability of event B given event A. Conditional probabilities are very important in medical decisions. By the end of the week, you will be able to solve problems using Bayes' rule, and update prior probabilities.</p><p>Please use the learning objectives and practice quiz to help you learn about Bayes' Rule, and apply what you have learned in the lab and on the quiz. ...
9 videos (Total 41 min), 4 readings, 3 quizzes
9 videos
Conditional Probabilities and Bayes' Rule2m
Bayes' Rule and Diagnostic Testing6m
Bayes Updating2m
Bayesian vs. frequentist definitions of probability4m
Inference for a Proportion: Frequentist Approach3m
Inference for a Proportion: Bayesian Approach7m
Effect of Sample Size on the Posterior2m
Frequentist vs. Bayesian Inference9m
Module Learning Objectivess
Week 1 Lab Instructions (RStudio)s
Week 1 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 1 Lab12m
Week 1 Practice Quiz20m
Week 1 Quiz20m
Week
2
7 hours to complete

## Bayesian Inference

In this week, we will discuss the continuous version of Bayes' rule and show you how to use it in a conjugate family, and discuss credible intervals. By the end of this week, you will be able to understand and define the concepts of prior, likelihood, and posterior probability and identify how they relate to one another....
10 videos (Total 45 min), 3 readings, 3 quizzes
10 videos
From the Discrete to the Continuous5m
Elicitation6m
Conjugacy4m
Inference on a Binomial Proportion5m
The Gamma-Poisson Conjugate Families6m
The Normal-Normal Conjugate Families3m
Non-Conjugate Priors4m
Credible Intervals3m
Predictive Inference4m
Module Learning Objectivess
Week 2 Lab Instructions (RStudio)s
Week 1 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 2 Lab28m
Week 2 Practice Quiz20m
Week 2 Quiz40m
Week
3
8 hours to complete

## Decision Making

In this module, we will discuss Bayesian decision making, hypothesis testing, and Bayesian testing. By the end of this week, you will be able to make optimal decisions based on Bayesian statistics and compare multiple hypotheses using Bayes Factors. ...
14 videos (Total 75 min), 3 readings, 3 quizzes
14 videos
Losses and decision making3m
Working with loss functions6m
Minimizing expected loss for hypothesis testing5m
Posterior probabilities of hypotheses and Bayes factors6m
The Normal-Gamma Conjugate Family6m
Inference via Monte Carlo Sampling3m
Predictive Distributions and Prior Choice5m
Reference Priors7m
Mixtures of Conjugate Priors and MCMC6m
Hypothesis Testing: Normal Mean with Known Variance7m
Comparing Two Paired Means Using Bayes' Factors6m
Comparing Two Independent Means: Hypothesis Testing3m
Comparing Two Independent Means: What to Report?5m
Module Learning Objectivess
Week 3 Lab Instructions (RStudio)s
Week 3 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 3 Lab22m
Week 3 Practice Quiz16m
Week 3 Quiz40m
Week
4
8 hours to complete

## Bayesian Regression

This week, we will look at Bayesian linear regressions and model averaging, which allows you to make inferences and predictions using several models. By the end of this week, you will be able to implement Bayesian model averaging, interpret Bayesian multiple linear regression and understand its relationship to the frequentist linear regression approach. ...
11 videos (Total 72 min), 3 readings, 3 quizzes
11 videos
Bayesian simple linear regression8m
Checking for outliers4m
Bayesian multiple regression4m
Model selection criteria5m
Bayesian model uncertainty7m
Bayesian model averaging7m
Stochastic exploration8m
Priors for Bayesian model uncertainty8m
R demo: crime and punishment9m
Decisions under model uncertainty7m
Module Learning Objectivess
Week 4 Lab Instructions (RStudio Cloud)s
Week 4 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 4 Lab22m
Week 4 Practice Quiz20m
Week 4 Quiz40m
Week
5
1 hour to complete

## Perspectives on Bayesian Applications

This week consists of interviews with statisticians on how they use Bayesian statistics in their work, as well as the final project in the course....
3 videos (Total 23 min), 1 reading
3 videos
Bayesian methods and big data: a talk with David Dunson8m
Bayesian methods in biostatistics and public health: a talk with Amy Herring4m
5 hours to complete

## Data Analysis Project

In this module you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment. ...
Project informations
3.9
158 Reviews

## 25%

started a new career after completing these courses

## 15%

got a tangible career benefit from this course

### Top Reviews

By RRSep 21st 2017

Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.

By GHApr 10th 2018

I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.

## Instructors

### Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

### David Banks

Professor of the Practice
Statistical Science

### Colin Rundel

Assistant Professor of the Practice
Statistical Science

### Merlise A Clyde

Professor
Department of Statistical Science

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world....

## About the Statistics with R Specialization

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions....