This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
What's inside
Five courses
Bayesian Statistics: From Concept to Data Analysis
Bayesian Statistics: From Concept to Data Analysis
Bayesian Statistics: Techniques and Models
(0 hours)
Bayesian Statistics: Techniques and Models
(0 hours)
Bayesian Statistics: Mixture Models
Bayesian Statistics: Mixture Models
Bayesian Statistics: Time Series Analysis
Bayesian Statistics: Time Series Analysis
Bayesian Statistics: Capstone Project
Bayesian Statistics: Capstone Project
Learning objectives
- Bayesian inference
- Time series forecasting
- Hierarchical modeling
- Bayesian inference
- Time series forecasting
- Hierarchical modeling
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