Certificate Data Analysis R Bayesian Approach
-- ViewingNowThe Certificate in Data Analysis R Bayesian Approach is a comprehensive course that focuses on using Bayesian methods for data analysis through the R programming language. This certification equips learners with essential skills in Bayesian inference, a powerful and increasingly popular approach in data analysis and machine learning.
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• Introduction to Bayesian Data Analysis: Understanding the basics of Bayesian theory, probability, and Bayes' theorem.
• R Programming for Bayesian Analysis: Learning R syntax, functions, and packages for Bayesian data analysis.
• Bayesian Inference: Understanding the concepts of prior and posterior distributions, likelihood functions, and Bayesian inference.
• Markov Chain Monte Carlo (MCMC) Methods: Exploring MCMC methods, including the Gibbs sampler, Metropolis-Hastings algorithm, and No-U-Turn Sampler (NUTS).
• Bayesian Linear Regression: Applying Bayesian methods to linear regression, including model specification, prior choice, and posterior estimation.
• Bayesian Generalized Linear Models: Extending Bayesian linear regression to generalized linear models, including logistic regression, poisson regression, and other GLMs.
• Model Selection and Comparison: Comparing Bayesian models, including model selection criteria, posterior predictive checks, and leave-one-out cross-validation.
• Bayesian Hierarchical Models: Developing Bayesian hierarchical models, including mixed-effects models, spatial models, and time-series models.
• Bayesian Nonparametric Models: Introducing Bayesian nonparametric methods, including Dirichlet processes, Chinese restaurant processes, and stick-breaking priors.
• Advanced Topics in Bayesian Data Analysis: Exploring advanced topics in Bayesian data analysis, including Bayesian model averaging, Bayesian computation, and scalable Bayesian inference.
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- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- ThreeFourHoursPerWeek
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