The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
The instructors for this course will be Dr. Srijith Rajamohan and Dr. Robert Settlage.
Overview
Syllabus
- Environment Setup
- Introduction to the compute environment for the Specialization. The users will be introduced to the Databricks Ecosystem for Data Science. The users can also deploy the notebooks to Binder for setup-free access.
- Introduction to the Fundamentals of Probability
- In this module, you will learn the foundations of probability and statistics. The focus is on gaining familiarity with terms and concepts.
- A Hands-On Introduction to Common Distributions
- Tis module will be an introduction to common distributions along with the Python code to generate, plot and interact with these distributions. You will also learn how to perform Maximum Likelihood Estimation (MLE) for various distributions and Kernel Density Estimation (KDE) for non-parametric distributions.
- Sampling Algorithms
- This module introduces you to various sampling algorithms for generating distributions. You will also be introduced to Python code that performs sampling.
Taught by
Dr. Srijith Rajamohan
