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October 4, 2024
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Bayesian Statistics for Modeling and Prediction. Learn the foundations and practice your data analysis skills.
Instructors: Matthew Heiner
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Intermediate level
Prior experience with calculus (you don’t need to remember how to do it, just to understand the concepts); an introductory statistics course.
(351 reviews)
Recommended experience
Intermediate level
Prior experience with calculus (you don’t need to remember how to do it, just to understand the concepts); an introductory statistics course.
Bayesian Inference
Time Series Forecasting
Hierarchical Modeling
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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.
Describe & apply the Bayesian approach to statistics.
Explain the key differences between Bayesian and Frequentist approaches.
Master the basics of the R computing environment.
Efficiently and effectively communicate the results of data analysis.
Use statistical modeling results to draw scientific conclusions.
Extend basic statistical models to account for correlated observations using hierarchical models.
Explain the basic principles behind the algorithm for fitting a mixture model.
Compute the expectation and variance of a mixture distribution.
Use mixture models to solve classification and clustering problems, and to provide density estimates.
Build models that describe temporal dependencies.
Use R for analysis and forecasting of times series.
Explain stationary time series processes.
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.
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This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
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.
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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
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