The whole tutorial schedule is here, along with registration information. And here are some details about the tutorials:
Bayesian statistics made simple
An introduction to Bayesian statistics using Python. Bayesian statistics are usually presented mathematically, but many of the ideas are easier to understand computationally. People who know Python can get started quickly and use Bayesian analysis to solve real problems. This tutorial is based on material and case studies from Think Bayes (O’Reilly Media).
Bayesian statistical methods are becoming more common and more important, but there are not many resources to help beginners get started. People who know Python can use their programming skills to get a head start.
I will present simple programs that demonstrate the concepts of Bayesian statistics, and apply them to a range of example problems. Participants will work hands-on with example code and practice on example problems.
Attendees should have at least basic level Python and basic statistics. If you learned about Bayes’s theorem and probability distributions at some time, that’s enough, even if you don’t remember it!
Attendees should bring a laptop with Python and matplotlib. You can work in any environment; you just need to be able to download a Python program and run it. I will provide code to help attendees get set up ahead of time.
Statistical inference with computational methods
Statistical inference is a fundamental tool in science and engineering, but it is often poorly understood. This tutorial uses computational methods, including Monte Carlo simulation and resampling, to explore estimation, hypothesis testing and statistical modeling. Attendees will develop understanding of statistical concepts and learn to use real data to answer relevant questions.
Do you know the difference between standard deviation and standard error? Do you know what statistical test to use for any occasion? Do you really know what a p-value is? How about a confidence interval?
Most students don’t really understand these concepts, even after taking several statistics classes. The problem is that these classes focus on mathematical methods that bury the concepts under a mountain of details.
This tutorial uses Python to implement simple statistical experiments that develop deep understanding. Attendees will learn about resampling and related tools that use random simulation to perform statistical inference, including estimation and hypothesis testing. We will use pandas, which provides structures for data analysis, along with NumPy and SciPy.
I will present examples using real-world data to answer relevant questions. The tutorial material is based on my book, Think Stats, a class I teach at Olin College, and my blog, “Probably Overthinking It.”
More information and registration here.
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