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.
Applied Learning Project
This Specialization trains the learner in the Bayesian approach to statistics, starting with the concept of probability all the way to the more complex concepts such as dynamic linear modeling. You will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data, and then dive deeper into the analysis of time series data.
The courses in this specialization combine lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience, while the culminating project is an opportunity for the learner to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you know to real-world data. You will review essential concepts in Bayesian statistics, learn and practice data analysis using R (an open-source, freely available statistical package), perform a complex data analysis on a real dataset, and compose a report on your methods and results.
