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.
Bayesian Statistics Specialization
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Bayesian Statistics Specialization
Bayesian Statistics for Modeling and Prediction. Learn the foundations and practice your data analysis skills.
Instructors: Matthew Heiner
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Get in-depth knowledge of a subject
4.6
(386 reviews)
Intermediate level
Recommended experience
2 months at 10 hours a week
Flexible schedule
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Share your expertise with employers
Get in-depth knowledge of a subject
4.6
(386 reviews)
Intermediate level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Bayesian Inference
Time Series Forecasting
Hierarchical Modeling
Overview
Skills you'll gain
- Technical Communication
- Mathematical Modeling
- Statistics
- Probability
- Bayesian Statistics
- Statistical Machine Learning
- Advanced Analytics
- Forecasting
- Statistical Modeling
- Statistical Methods
- Markov Model
- Data Analysis
- Probability Distribution
- Statistical Analysis
- Statistical Inference
- Probability & Statistics
- Data Science
- Time Series Analysis and Forecasting
Tools you'll learn
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
62 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from University of California, Santa Cruz
Specialization - 5 course series
What you'll learn
Describe & apply the Bayesian approach to statistics.
Explain the key differences between Bayesian and Frequentist approaches.
Master the basics of the R computing environment.
Skills you'll gain
Category: Bayesian Statistics
Category: Probability Distribution
Category: Probability
Category: Statistical Inference
Category: Regression Analysis
Category: Data Analysis
Category: R Programming
Category: Statistical Modeling
Category: Statistical Analysis
Category: Statistics
Category: Microsoft Excel
What you'll learn
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.
Skills you'll gain
Category: Bayesian Statistics
Category: Statistical Modeling
Category: R Programming
Category: Probability Distribution
Category: Regression Analysis
Category: Data Analysis
Category: Simulations
Category: Statistical Inference
Category: Markov Model
Category: Statistical Analysis
Category: Statistical Methods
Category: Probability
What you'll learn
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.
Skills you'll gain
Category: Bayesian Statistics
Category: Probability & Statistics
Category: Statistical Modeling
Category: Statistical Methods
Category: Mathematical Modeling
Category: Machine Learning Algorithms
Category: Markov Model
Category: Unsupervised Learning
Category: Statistical Machine Learning
Category: R Programming
What you'll learn
Build models that describe temporal dependencies.
Use R for analysis and forecasting of times series.
Explain stationary time series processes.
Skills you'll gain
Category: Time Series Analysis and Forecasting
Category: Bayesian Statistics
Category: Forecasting
Category: Mathematical Modeling
Category: Statistical Modeling
Category: Data Analysis
Category: Statistical Inference
Category: Probability Distribution
Category: R Programming
Category: Statistical Analysis
Category: Regression Analysis
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
Category: Probability Distribution
Category: Time Series Analysis and Forecasting
Category: Markov Model
Category: R Programming
Category: Data Analysis
Category: Technical Communication
Category: Statistical Methods
Category: Advanced Analytics
Category: Statistical Analysis
Category: Sampling (Statistics)
Category: Predictive Modeling
Category: Statistical Modeling
Category: Bayesian Statistics
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Frequently asked questions
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.
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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. 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.
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