Applied Bayesian Data Analysis
About this course
This Specialization is designed for data scientists, analysts, and applied scientists seeking to develop expertise in Bayesian statistical methods and probabilistic modeling. Through three comprehensive courses, learners will master foundational Bayesian inference techniques, such as Bayes rule for distributions, conjugate priors and MCMC methods. The curriculum progresses to advanced topics including Bayesian regression, hierarchical models, generalized linear models, variational inference, and Bayesian non-parametric methods. Students will gain hands-on experience with modern probabilistic programming tools and apply Bayesian techniques to real-world applications in sports analytics, healthcare, and business decision-making.
83/100
CourseAsk score
- What the provider tells you
- 32/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
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What you'll learn
- understand Bayesian inference techniques
- apply MCMC methods
- construct Bayesian regression models
- analyze hierarchical models
- implement variational inference techniques
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