Global Certificate in Bayesian Modeling for Data Science
-- ViewingNowThe Global Certificate in Bayesian Modeling for Data Science is a comprehensive course that emphasizes the importance of Bayesian methods in data science. In an era where businesses rely heavily on data-driven decision-making, this certificate course stands out with its focus on Bayesian theory and practical applications.
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⢠Introduction to Bayesian Modeling: Basic concepts, principles, and benefits of Bayesian modeling in data science. Understanding the Bayes theorem and its application in statistical modeling.
⢠Probability Distributions: Overview of common probability distributions, including normal, binomial, multinomial, Poisson, and exponential distributions. Understanding the properties and applications of these distributions in Bayesian modeling.
⢠Graphical Models: Introduction to directed acyclic graphs (DAGs), plate notation, and conditional probability distributions. Understanding the use of graphical models to represent complex relationships in Bayesian modeling.
⢠Conjugate Priors: Overview of conjugate priors and their importance in Bayesian modeling. Understanding the concept of prior-posterior convergence and the use of conjugate priors to simplify computations.
⢠MCMC Methods: Overview of Markov Chain Monte Carlo (MCMC) methods, including Metropolis-Hastings, Gibbs sampling, and Hamiltonian Monte Carlo (HMC). Understanding the theory and implementation of MCMC methods in Bayesian modeling.
⢠Bayesian Inference: Inference in Bayesian modeling, including credible intervals, posterior predictive distributions, and model comparison. Understanding the use of Bayesian inference to make predictions and draw conclusions from data.
⢠Python for Bayesian Modeling: Overview of Python libraries for Bayesian modeling, including NumPy, SciPy, and PyMC3. Understanding the use of these libraries to implement Bayesian models in practice.
⢠Case Studies in Bayesian Modeling: Real-world applications of Bayesian modeling in data science, including examples from finance, healthcare, and social sciences. Understanding the use of Bayesian modeling to solve complex problems in practice.
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