Idea for the outline for learning Bayesian methods as a Data Scientist
- 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.
- Learn about the different types of models used in Bayesian statistics, such as conjugate priors and hierarchical models.
- 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.
- Learn about variational inference, which is an alternative to MCMC that is useful for approximating Bayesian inference in large or complex models.
- Practice implementing Bayesian models using popular tools and software such as PyMC3, Stan, and Edward.
- Get familiar with Bayesian deep learning frameworks such as PyMC, TensorFlow Probability, and Edward2.
- Learn how to interpret and visualize the results of Bayesian models and perform model comparison and selection.
- Apply Bayesian methods to real-world data science problems and practice communicating the results to non-technical stakeholders.
- Read the literature and keep updated on the latest developments in Bayesian methods and the related field.
- "Bayesian Data Analysis" Third Edition by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin
- "Machine Learning" by Tom Mitchell
- "The Hundred-Page Machine Learning Book" by Andriy Burkov
- "Introduction to Machine Learning with Python" by Andreas Müller and Sarah Guido.