Bayesian statistical methods offer a powerful set of tools to tackle a wide variety of data science problems. In addition, the Bayesian approach generates results that are easy to interpret and automatically account for uncertainty in quantities that we wish to estimate and predict. Historically, computational challenges have been a barrier, particularly to new users, but there now exists a mature set of probabilistic programming tools that are both capable and easy to learn. We will use the newest release of PyMC (version 4) in this tutorial, but the concepts and approaches that will be taught are portable to any probabilistic programming framework. This tutorial is intended for practicing and aspiring data scientists and analysts looking to learn how to apply Bayesian statistics and probabilistic programming to their work. It will provide learners with a high-level understanding of Bayesian statistical methods and their potential for use in a variety of applications. They will also gain hands-on experience with applying these methods using PyMC, specifically including the specification, fitting and checking of models applied to a couple of real-world datasets. PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/c/PyDataTV/videos https://www.youtube.com/watch?v=woXdiyezCPA

Mobile UX design is tricky. There are so many things we have to consider, including the growing list of mobile devices, the ways people interact with them, and the fact that people want consistent experiences across all device types. To create great mobile UX, you need to follow the best practices developed over the last several years. This video presents some of the rules and guidelines that will help even a beginner to create top-notch mobile UX design. https://www.youtube.com/watch?v=EJISRhrmqds

Python comes with many standard library packages included without any "pip install"! In this beginners tutorial we will go through a few of these with some interactive challenges during the session. Specifically we will dive into pathlib, datetime, collections, itertools and functools and how these can help you. Github: https://github.com/simonwardjones/pydata-talk-2022 PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/c/PyDataTV https://www.youtube.com/watch?v=2UeA2FRs8-Q