Bayesian Statistics: Capstone Project

Ends soon! Save on skills that make you shine with 40% off 3 months of Coursera Plus. Save now

Bayesian Statistics: Capstone Project

This course is part of Bayesian Statistics Specialization

Instructor: Jizhou Kang

1,704 already enrolled

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Advanced level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Advanced level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Demonstrate a wide range of skills and knowledge in Bayesian statistics.
Explain essential concepts in Bayesian statistics.
Apply what you know to real-world data.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments¹

AI Graded see disclaimer

Taught in English

91%

of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Bayesian Statistics Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

This is the capstone project for UC Santa Cruz's Bayesian Statistics Specialization. It is an opportunity for you 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 with lecture videos and quizzes, and you will perform a complex data analysis and compose a report on your methods and results.

In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.

What's included

3 videos7 readings2 assignments

3 videosTotal 26 minutes

Introduction4 minutes
Model Formulation13 minutes
Prediction for AR Models9 minutes

7 readingsTotal 70 minutes

Prerequisite skill checklist10 minutes
Read Data10 minutes
Review: Useful Distributions10 minutes
Posterior Distribution Derivation10 minutes
AR model fitting example10 minutes
AR model prediction example10 minutes
Extended AR model10 minutes

2 assignmentsTotal 60 minutes

Practice Quiz for Week 130 minutes
First step for the project30 minutes

In this module, we will introduce some criteria that can be used in selecting the order of AR processes and the number of mixing components, which will be used later when we introduce mixture of AR models.

What's included

2 videos2 readings2 assignments

In this module, we will perform Bayesian analysis for location mixture of AR(p) models.

What's included

4 videos3 readings2 assignments

4 videosTotal 45 minutes

Prediction for Location Mixture of AR Models5 minutes
Full conditional distributions of model parameters26 minutes
Coding the Gibbs sampler9 minutes
Prediction for location mixture of AR model5 minutes

3 readingsTotal 30 minutes

Sample code for the Gibbs sampler10 minutes
Determine the number of components10 minutes
Location and scale mixture of AR model10 minutes

2 assignmentsTotal 60 minutes

Fit a location mixture of AR model30 minutes
Determine number of components for the mixture model30 minutes

In this module, we will use everything we have learned up until now to perform a mixture model on time series data.

What's included

1 reading1 peer review

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Jizhou Kang

University of California, Santa Cruz

1 Course1,704 learners

Offered by

University of California, Santa Cruz

Explore more from Data Analysis

University of California, Santa Cruz
Bayesian Statistics: From Concept to Data Analysis
Course
University of California, Santa Cruz
Bayesian Statistics: Techniques and Models
Course
University of California, Santa Cruz
Bayesian Statistics
Specialization
Duke University
Bayesian Statistics
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.