Learning Bayesian Methods as Data Scientist

Idea for the outline for learning Bayesian methods as a Data Scientist

1. Begin by understanding the basic concepts of probability and Bayesian statistics. This includes understanding probability distributions, Bayes' theorem, and the concept of a prior and a posterior.
2. Learn about the different types of models used in Bayesian statistics, such as conjugate priors and hierarchical models.
3. Learn about Markov Chain Monte Carlo (MCMC) methods, which are a class of algorithms used to perform Bayesian inference. These include Metropolis-Hastings, Gibbs sampling and Hamiltonian Monte Carlo.
4. Learn about variational inference, which is an alternative to MCMC that is useful for approximating Bayesian inference in large or complex models.
5. Practice implementing Bayesian models using popular tools and software such as PyMC3, Stan, and Edward.
6. Get familiar with Bayesian deep learning frameworks such as PyMC, TensorFlow Probability, and Edward2.
7. Learn how to interpret and visualize the results of Bayesian models and perform model comparison and selection.
8. Apply Bayesian methods to real-world data science problems and practice communicating the results to non-technical stakeholders.
9. Read the literature and keep updated on the latest developments in Bayesian methods and the related field.

References

1. "Bayesian Data Analysis" Third Edition by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
2. "Machine Learning" by Tom Mitchell
3. "The Hundred-Page Machine Learning Book" by Andriy Burkov
4. "Introduction to Machine Learning with Python" by Andreas Müller and Sarah Guido.

X::Learn Bayesian Methods in 4 Steps - By Reading and by Doing