Coursera - Applied Bayesian Data Analysis Specialization
Category: Tutorials
- views: 145
- date: 9 June 2026
- posted by: 0nelove
Coursera - Applied Bayesian Data Analysis Specialization
Released 5/2026
By Konstantinos Pelechrinis - University of Pittsburgh
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 61 Lessons ( 4h 10m ) | Size: 1.4 GB
Master Bayesian Methods for Data Analysis.
What you'll learn
⚡ Apply Bayes' theorem, conjugate priors, and MCMC methods to perform Bayesian inference and construct credible intervals for parameter estimation.
⚡ Build and validate Bayesian regression models including linear, hierarchical, and GLM models for predictive analytics and model comparison.
⚡ Implement advanced Bayesian methods—variational inference and non-parametric modeling—for complex data analysis and Bayesian decision theory.
⚡ Apply probabilistic programming and Bayesian workflows to real-world applications in sports analytics, healthcare, and data-driven decision-making.
Skills you'll gain
? Bayesian Statistics
? Data Analysis
? Data Science
? Machine Learning
? Markov Model
? Mathematical Modeling
? Model Evaluation
? Predictive Analytics
? Predictive Modeling
? Probability & Statistics
? Probability Distribution
? Regression Analysis
? Sampling (Statistics)
? Statistical Analysis
? Statistical Inference
? Statistical Machine Learning
? Statistical Modeling
? Statistical Programming
? Statistics
? Show all
Tools you'll learn
? Python Programming
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.
Applied Learning Project
Learners will complete hands-on projects that demonstrate practical application of Bayesian methods to real-world problems. Projects include implementing MCMC algorithms for parameter estimation, building Bayesian regression models for predictive analytics, developing hierarchical Bayesian models for multi-level data, performing Bayesian model selection and comparison, and applying advanced Bayesian techniques to domain-specific problems in sports analytics and medical decision-making under uncertainty.
Homepage
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