About this Course

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Learner Career Outcomes

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.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 35 hours to complete
English

Skills you will gain

Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming

Learner Career Outcomes

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.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 35 hours to complete
English

Offered by

Placeholder

Duke University

Syllabus - What you will learn from this course

Content RatingThumbs Up78%(3,557 ratings)Info
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
4 readings
About Statistics with R Specialization10m
About Bayesian Statistics10m
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
4 readings
Module Learning Objectives2h
About Lab Choices10m
Week 1 Lab Instructions (RStudio)2h
Week 1 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 1 Lab30m
Week 1 Practice Quiz20m
Week 1 Quiz30m
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
3 readings
Module Learning Objectives2h
Week 2 Lab Instructions (RStudio)3h
Week 1 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 2 Lab30m
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
3 readings
Module Learning Objectives2h
Week 3 Lab Instructions (RStudio)3h
Week 3 Lab Instructions (RStudio Cloud)10m
3 practice exercises
Week 3 Lab30m
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
3 readings
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

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Statistics with R

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