Introduction to Computational Statistics for Data Scientists

The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Hypothesis Significance Testing (NHST). What it does cover is: The basics of Bayesian statistics and probability Understanding Bayesian inference and how it works The bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot. ly A scalable Python-based framework for performing Bayesian inference, i.e. PyMC3 With this goal in mind, the content is divided into the following three main sections (courses). Introduction to Bayesian Statistics - The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1. Introduction to Monte Carlo Methods - This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2. PyMC3 for Bayesian Modeling and Inference - PyMC3 will be introduced along with its application to some real world scenarios. The lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks.

Created by: Databricks

Language: English

Find Out More
Share
Facebook
Twitter
Pinterest
Reddit
StumbleUpon
LinkedIn
Email

Cal Poly Online Courses

Back to Top

Log In

Contact Us

Upload An Image

Please select an image to upload
Note: must be in .png, .gif or .jpg format
OR
Provide URL where image can be downloaded
Note: must be in .png, .gif or .jpg format

By clicking this button,
you agree to the terms of use

By clicking "Create Alert" I agree to the Uloop Terms of Use.

Image not available.

Add a Photo

Please select a photo to upload
Note: must be in .png, .gif or .jpg format