This Specialization in Bayesian Statistics is designed to equip learners with proficiency in Bayesian methods, Bayesian inference, R programming, and more. It comprises four complete courses, including "From Concept to Data Analysis," "Techniques and Models," "Mixture Models," and "Time Series Analysis," along with a culminating project. Through engaging in-depth study, students will delve into essential topics such as Bayesian inference, time series forecasting, and hierarchical modeling.
Throughout the program, participants will develop a strong foundation in statistics and learn to apply Bayesian methods to real-world data. The courses will cover topics such as conjugate models, Markov chain Monte Carlo (MCMC), mixture models, and dynamic linear modeling. Learners will also gain practical experience in analyzing data, forecasting, and creating statistical models.
Certificate Available ✔
Get Started / More Info
This Specialization comprises four comprehensive courses: "From Concept to Data Analysis," "Techniques and Models," "Mixture Models," and "Time Series Analysis," along with a culminating capstone project.
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.
Statistics with Python is a specialization teaching statistical analysis using Python. Learners will master data visualization, inferential analysis, and statistical...
Causal Inference is a Master's level course providing a rigorous mathematical survey of inferring causation, offering methods to estimate causal relationships and...
This course, "Inferenzstatistik," equips learners with essential statistical inference methods for numerical and categorical data analysis, focusing on...
Stability and Capability in Quality Improvement is a comprehensive course focusing on analyzing process stability, statistical control, and capability for quality...