Applied Bayesian for Analytics
Bayesian Statistics is a captivating field and is used most prominently in data sciences. In this course we will learn about the foundation of Bayesian concepts, how it differs from Classical Statistics including among others Parametrizations, Priors, Likelihood, Monte Carlo methods and computing Bayesian models with the exploration of Multilevel modelling.
This course is divided into two parts i.e. Theoretical and Empirical part of Bayesian Analytics. First three weeks cover the Theoretical part which includes how to form a prior, how to calculate a posterior and several other aspects. Rest of the weeks will cover the empirical part which explains how to compute Bayesian modelling. Completion of this course will provide you with an understanding of the Bayesian approach, the primary difference between Bayesian and Frequentist approaches and experience in data analyses.
Upcoming start dates
- Self-paced Online
Who should attend?
Basic understanding of Statistics
- What is Bayesian Statistics and How it is different than Classical Statistics
- Bayesian analysis of Simple Models
- Monte Carlo Methods
- Computational Bayes
- Bayesian Linear Models
- Bayesian Hierarchical Models
Course delivery details
This course is offered through Indian Institute of Management Bangalore, a partner institute of EdX.
2-3 hours per week
- Verified Track -$149
- Audit Track - Free
Certification / Credits
What you'll learn
- Understand the necessary Bayesian concepts from practical point of view for better decision making.
- Learn Bayesian approach to estimate likely event outcomes, or probabilities using datasets.
- Gain “hands on” experience in creating and estimating Bayesian models using R and OPENBUGS.
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