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## 22%

started a new career after completing these courses

## 17%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 35 hours to complete
English
Subtitles: English, Korean

## Skills you will gain

Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming

## 22%

started a new career after completing these courses

## 17%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 35 hours to complete
English
Subtitles: English, Korean

## Syllabus - What you will learn from this course

Content Rating79%(3,441 ratings)
Week
1

## Week 1

1 hour to complete

## About the Specialization and the Course

1 hour to complete
1 video (Total 2 min), 4 readings
1 video
Pre-requisite Knowledge10m
Special Thanks2m
6 hours to complete

## The Basics of Bayesian Statistics

6 hours to complete
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 Objectives2h
Week 1 Lab Instructions (RStudio)2h
Week 1 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 1 Lab12m
Week 1 Practice Quiz20m
Week 1 Quiz20m
Week
2

## Week 2

7 hours to complete

## Bayesian Inference

7 hours to complete
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 Objectives2h
Week 2 Lab Instructions (RStudio)3h
Week 1 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 2 Lab28m
Week 2 Practice Quiz20m
Week 2 Quiz40m
Week
3

## Week 3

8 hours to complete

## Decision Making

8 hours to complete
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 Objectives2h
Week 3 Lab Instructions (RStudio)3h
Week 3 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 3 Lab22m
Week 3 Practice Quiz16m
Week 3 Quiz40m
Week
4

## Week 4

8 hours to complete

## Bayesian Regression

8 hours to complete
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 Objectives2h
Week 4 Lab Instructions (RStudio Cloud)3h
Week 4 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 4 Lab22m
Week 4 Practice Quiz20m
Week 4 Quiz40m

## 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....

• Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

• The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
• The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.