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ABSTRACT
Humans constantly struggle with uncertainty, both in life and in data analysis. It’s impossible to collect all the relevant data to answer any particular question, so there is necessarily uncertainty in our analysis. As such, we need to quantify the uncertainty and from that judge our results. Traditional statistical methods (also called frequentist methods) such as hypothesis testing and confidence intervals often don’t address this appropriately. For example, we typically want to know the probability that a parameter falls in some range, but this type of analysis is unavailable from a frequentist perspective. Developing a statistical model with frequentist methods is often out of reach for typical data analysts so they are left asking “What test do I apply to this data?” rather than modeling their specific problem. Bayesian statistics offers a better approach to understanding the uncertainty in our data and answering the questions we want to ask. In a Bayesian framework, we combine prior knowledge with the data to produce a probability distribution that models and quantifies the uncertainty in the data. Using Python packages such as PyMC and Sampyl, the richness and clarity of Bayesian data analysis is available to anyone.

BIO:
Mat received a PhD in Physics from UC Berkeley where he studied the neural correlations of short-term memory in prefrontal cortex. During that time, he picked up Python, machine learning, and a love for education. He’s been at Udacity for over two years, developing content for various data science courses including the Deep Learning Nanodegree program. Mat is also the author of Sampyl, a Python library for Bayesian data analysis, and SeekWell, a library that improves the usage of SQL within Python.

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