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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โข 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.
โข 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.
โข 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.
โข 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.
โข 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.
โข 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.
โข 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.
โข 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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