Undergraduate Certificate in Bayesian Statistics with R
-- viewing nowThe Undergraduate Certificate in Bayesian Statistics with R is a comprehensive course designed to equip learners with the essential skills required in the data-driven industry. This program focuses on teaching Bayesian statistics, a modern approach to statistics that is increasingly being used by data professionals worldwide.
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Course Details
• Basic Statistical Concepts with R: data exploration, data visualization, probability distributions, and summary statistics.
• Introduction to Bayesian Inference: prior and posterior distributions, likelihood functions, and Bayes' theorem.
• Bayesian Modeling with R: using Markov Chain Monte Carlo (MCMC), Gibbs sampling, and Stan.
• Model Checking and Comparison: graphical and numerical methods for model assessment, deviance information criterion (DIC), and posterior predictive checks.
• Linear Regression in Bayesian Framework: simple and multiple linear regression models with R.
• Generalized Linear Models: Bayesian logistic regression, Poisson regression, and other GLMs.
• Hierarchical Modeling: mixed-effects models, random effects, and multilevel models.
• Time Series Analysis with Bayesian Methods: ARIMA, state space models, and Kalman filter.
• Bayesian Networks and Causal Inference: introduction to DAGs, Bayesian networks, and causal inference with R.
Career Path
Entry Requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course Status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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