This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models.
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About this Course
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Flexible deadlines
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Shareable Certificate
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 4 of 5 in the
Intermediate Level
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
Approx. 22 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessSkills you will gain
- Bayesian Statistics
- Forecasting
- Dynamic Linear Modeling
- Time Series
- R Programming
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Course 4 of 5 in the
Intermediate Level
Familiarity with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference.
Approx. 22 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
6 hours to complete
Week 1: Introduction to time series and the AR(1) process
6 hours to complete
9 videos (Total 94 min), 12 readings, 5 quizzes
5 hours to complete
Week 2: The AR(p) process
5 hours to complete
9 videos (Total 96 min), 8 readings, 3 quizzes
5 hours to complete
Week 3: Normal dynamic linear models, Part I
5 hours to complete
10 videos (Total 114 min), 7 readings, 3 quizzes
4 hours to complete
Week 4: Normal dynamic linear models, Part II
4 hours to complete
7 videos (Total 103 min), 4 readings, 3 quizzes
About the Bayesian Statistics Specialization

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