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Introduction to Computational Statistics for Data Scientists Specialization

Practical Bayesian Inference. A​ conceptual understanding of the techniques and the tools used to perform scalable Bayesian inference in practice with PyMC3.
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 Null 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.
Applied Learning Project
Implement Distributions in Python and visualize it statically using Matplotlib or Seaborn and interactively using Plot.ly.

Implement Monte Carlo Sampling algorithms in Python.

Learn the basics of PyMC3 for various Bayesian modeling including Linear Regression, Hierarchical Regression, Classification, Robust models and assessing the quality of models.

Use PyMC3 to model the disease dynamics of and infer the parameters of an SIR model of COVID-19 from real-world data.

Course Information

Estimated Time: Approximately 3 months to complete Suggested pace of 4 hours/week

Difficulty: Beginner

Free

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Course Information

Estimated Time: Approximately 3 months to complete Suggested pace of 4 hours/week

Difficulty: Beginner