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Bayesian StatisticsBayesian Linear RegressionBayesian InferenceR Programming

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강의 계획 - 이 강좌에서 배울 내용

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1

1

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About the Specialization and the Course

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1개 동영상 (총 2분), 4 개의 읽기 자료
1개의 동영상
4개의 읽기 자료
About Statistics with R Specialization10m
About Bayesian Statistics10m
Pre-requisite Knowledge10m
Special Thanks2m
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The Basics of Bayesian Statistics

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9개 동영상 (총 41분), 4 개의 읽기 자료, 3 개의 테스트
9개의 동영상
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
4개의 읽기 자료
Module Learning Objectives2시간
About Lab Choices10m
Week 1 Lab Instructions (RStudio)2시간
Week 1 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 1 Lab12m
Week 1 Practice Quiz20m
Week 1 Quiz20m
2

2

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Bayesian Inference

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10개 동영상 (총 45분), 3 개의 읽기 자료, 3 개의 테스트
10개의 동영상
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
3개의 읽기 자료
Module Learning Objectives2시간
Week 2 Lab Instructions (RStudio)3시간
Week 1 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 2 Lab28m
Week 2 Practice Quiz20m
Week 2 Quiz40m
3

3

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Decision Making

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14개 동영상 (총 75분), 3 개의 읽기 자료, 3 개의 테스트
14개의 동영상
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
3개의 읽기 자료
Module Learning Objectives2시간
Week 3 Lab Instructions (RStudio)3시간
Week 3 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 3 Lab22m
Week 3 Practice Quiz16m
Week 3 Quiz40m
4

4

완료하는 데 8시간 필요

Bayesian Regression

완료하는 데 8시간 필요
11개 동영상 (총 72분), 3 개의 읽기 자료, 3 개의 테스트
11개의 동영상
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
3개의 읽기 자료
Module Learning Objectives2시간
Week 4 Lab Instructions (RStudio Cloud)3시간
Week 4 Lab Instructions (RStudio Cloud)10m
3개 연습문제
Week 4 Lab22m
Week 4 Practice Quiz20m
Week 4 Quiz40m

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Statistics with R 특화 과정 정보

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....
Statistics with R

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  • 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.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • We assume you have knowledge equivalent to the prior courses in this specialization.

  • No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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