Xomnia is a word combining the letter ‘X’ – the unknown – and “Omnia” – Latin for everything. Our team of data scientists and big data engineers are trained to find the undefined – X – in all the relevant data sources – Omnia. This unknown – X – is untapped business value. Combining the X and Omnia you get the Xomnia spirit. Eager, curious and dedicated people, who have the belief that the future is big data.
The world isn’t black and white; it’s Bayesian. These statistical models have a solid mathematical foundation to not only give a clear cut result, but also return the confidence in that result. This makes the models easy to interpret and incorporate in real-world decision making. And that’s not all: with some domain expertise, a Bayesian model will also perform well on small, noisy datasets. It should come as no surprise that Bayesian statistics are applied in many different fields, from e-commerce to healthcare.
During this course, you will learn how to think Bayesian: you will apply Bayesian statistics to a number of practical use cases, and learn about various relevant concepts in the process. MCMC-methods, mixture models, and partial pooling will all make an appearance during this course, however, the focus is practical rather than theoretical. At the end of the course, you’ll be able to build Bayesian models using PyMC3.
Requirements: You should have a good grasp of Python, pandas, and general coding skills before enrolling in this course. A basic understanding of calculus and some statistics and probably theory is also required.