WHAT YOU WILL LEARN
Bayesian Inference
Time Collection Forecasting
Hierarchical Modeling
SKILLS YOU WILL GAIN
- Bayesian Statistics
- Knowledge Science
- R Programming
- Knowledge Evaluation
- Statistics
- Bayesian Inference
- Gibbs Sampling
- Markov Mannequin
- Combination Mannequin
- Forecasting
- Dynamic Linear Modeling
- Time Collection
About this Specialization
This Specialization is meant for all learners searching for to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and way more. By way of 4 full programs (From Idea to Knowledge Evaluation; Methods and Fashions; Combination Fashions; Time Collection Evaluation) and a culminating mission, you’ll cowl Bayesian strategies — akin to conjugate fashions, MCMC, combination fashions, and dynamic linear modeling — which can offer you the abilities essential to carry out evaluation, have interaction in forecasting, and create statistical fashions utilizing real-world information.
Utilized Studying Challenge
This Specialization trains the learner within the Bayesian method to statistics, beginning with the idea of chance all the best way to the extra advanced ideas akin to dynamic linear modeling. You’ll be taught concerning the philosophy of the Bayesian method in addition to the way to implement it for widespread forms of information, after which dive deeper into the evaluation of time sequence information.
The programs on this specialization mix lecture movies, laptop demonstrations, readings, workouts, and dialogue boards to create an lively studying expertise, whereas the culminating mission is a chance for the learner to exhibit a variety of expertise and information in Bayesian statistics and to use what you already know to real-world information. You’ll evaluate important ideas in Bayesian statistics, be taught and follow information evaluation utilizing R (an open-source, freely out there statistical package deal), carry out a fancy information evaluation on an actual dataset, and compose a report in your strategies and outcomes.
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