Overview
This Bayesian Statistics course offered by Coursera in partnership with University of California, Santa Cruz 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.
What You Will Learn:
- Bayesian Inference
- Hierarchical Modeling
- Time Series Forecasting
Skills You Will Gain:
- Statistics
- Data Science
- Bayesian Statistics
- Data Analysis
- R Programming
Programme Structure
Courses include:
- Bayesian Statistics: From Concept to Data Analysis
- Bayesian Statistics: Techniques and Models
- Bayesian Statistics: Mixture Models
- Bayesian Statistics: Time Series Analysis
- Bayesian Statistics: Capstone Project
Key information
Duration
- Part-time
- 2 months
- Flexible
Start dates & application deadlines
Language
Delivered
Disciplines
Statistics View 86 other Short Courses in Statistics in United StatesAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
Intermediate level
- Recommended experience: Prior experience with calculus (you don’t need to remember how to do it, just to understand the concepts); an introductory statistics course.
Tuition Fee
-
International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 months. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 2 months.
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Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.