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 Null Hypothesis Significance Testing (NHST).
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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.Auto Summary
"Introduction to Computational Statistics for Data Scientists" is a foundational course designed for aspiring or new data scientists who want to master the basics of Bayesian statistics and probability, specifically for performing inference. This course does not cover the basics of statistics and probability nor Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). Instead, it focuses on: - The fundamentals of Bayesian statistics and probability - Understanding and applying Bayesian inference - Utilizing essential Python tools such as NumPy, Pandas, Scipy, Matplotlib, Seaborn, and Plot.ly for Bayesian analysis - Implementing a scalable Python framework for Bayesian inference using PyMC3 The course is structured into three main sections: 1. **Introduction to Bayesian Statistics**: Learn the basics of probability, Bayesian modeling, and inference. 2. **Introduction to Monte Carlo Methods**: Explore methods for approximate inference when exact calculations are impractical. 3. **PyMC3 for Bayesian Modeling and Inference**: Apply PyMC3 to real-world scenarios and gain hands-on experience. Delivered through interactive Jupyter notebooks, this course ensures that participants can engage directly with the content. Offered by Coursera, it is available via Starter and Professional subscriptions, making it accessible to a wide range of learners eager to develop their computational statistics skills in the IT and computer science domain.
Dr. Srijith Rajamohan