Bayesian Statistics: From Concept to Data Analysis
Master the fundamentals of Bayesian statistics, from probability theory to practical data analysis using R or Excel. Perfect for intermediate statisticians.
Master the fundamentals of Bayesian statistics, from probability theory to practical data analysis using R or Excel. Perfect for intermediate statisticians.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Bayesian Statistics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.6
(3,156 ratings)
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What you'll learn
Master the fundamental concepts of Bayesian statistics and probability
Implement Bayesian data analysis using R or Excel
Understand key differences between Bayesian and Frequentist approaches
Apply Bayes' theorem to real-world statistical problems
Develop skills in statistical modeling and inference
Perform Bayesian analysis on both discrete and continuous data
Skills you'll gain
This course includes:
3.8 Hours PreRecorded video
18 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces the Bayesian approach to statistics, bridging theoretical concepts with practical applications. Students learn fundamental probability theory, Bayesian inference, and data analysis techniques using both R and Excel. The curriculum covers conjugate priors, model selection, and comparison with frequentist approaches, providing hands-on experience through computer demonstrations and exercises.
Probability and Bayes' Theorem
Module 1 · 3 Hours to complete
Statistical Inference
Module 2 · 3 Hours to complete
Priors and Models for Discrete Data
Module 3 · 2 Hours to complete
Models for Continuous Data
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Leading Expert in Applied Bayesian Statistics at UC Santa Cruz
Herbert Lee is a Professor of Statistics in the Jack Baskin School of Engineering at the University of California, Santa Cruz, where he also serves as Vice Provost for Academic Affairs. He earned his B.S. in Mathematics from Yale University and both his M.S. and Ph.D. in Statistics from Carnegie Mellon University. After completing a postdoctoral fellowship at Duke University, he joined the UCSC faculty in 2002. As an applied Bayesian statistician, his research interests encompass computer simulation experiments, inverse problems, optimization, spatial statistics, classification and clustering, and neural networks. He has authored two books, Bayesian Nonparametrics via Neural Networks and Multiscale Modeling: A Bayesian Perspective (co-authored with Marco Ferreira), along with a variety of academic papers.
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4.6 course rating
3,156 ratings
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