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Introduction to Bayesian Statistics
  • Level Foundation
  • Duration 13 hours
  • Course by Databricks
  • Offered by

About

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.

Modules

Welcome!

    • 4 Videos
    • 1 Readings
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4 Videos

  • Welcome to the Specialization!
  • Welcome to Course 1!
  • Python Environment Setup
  • Introduction to the Databricks Ecosystem for Data Science

1 Readings

  • What can you expect from this course/specialization?

Belief and probability

    • 3 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Week 2 Belief and Probability Practice
  • Week 2 Belief and Probability Graded Quiz

3 Videos

  • Introductions
  • Chance regularities and random processes
  • Outcomes, events and spaces

1 Readings

  • Introduction and references

Rules for manipulating probability

    • 3 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Week 2 Manipulating Probability Practice
  • Week 2 Manipulating Probability Graded Quiz

3 Videos

  • Addition rules of probability
  • Multiplication rules of probability
  • Conditional probability, Random Variables and Experiments

1 Readings

  • Rules for manipulating probability

Introduction to distributions

    • 3 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Week 2 Distributions Practice
  • Week 2 Distributions Graded Quiz

3 Videos

  • Random Variables and Distributions
  • Moments, mean and variance
  • Joint distributions of Random Variables

1 Readings

  • Random variables

Estimation using MoM and MLE

    • 1 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Week 2 Estimation Practice
  • Week 2 Estimation Graded Quiz

1 Videos

  • Estimation using MoM and MLE

1 Readings

  • MoM and MLE

Decisions, loss and priors

    • 2 Videos
    • 2 Readings
    • 2 Assignment
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2 Assignment

  • Week 2 Decisions Practice
  • Week 2 Decisions Graded Quiz

2 Videos

  • Basics of Bayes' Rule
  • Decisions and Loss Functions

2 Readings

  • Bayes' and decisions
  • Loss functions

More on Priors

    • 5 Videos
    • 1 Readings
    • 2 Assignment
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2 Assignment

  • Week 2 Priors Practice
  • Week 2 Priors Graded Quiz

5 Videos

  • Priors introduction
  • Priors as conjugates
  • Informative vs non-informative priors
  • Jeffrey's Prior
  • Prior distributions and posterior ramifications

1 Readings

  • More on priors

Some Common Distributions

    • 9 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Common distributions

9 Videos

  • The Binomial Distribution
  • Negative Binomial Distribution
  • Poisson Distribution
  • Exponential Distribution
  • Gamma Distribution
  • Normal Distribution
  • Lognormal Distribution
  • Student's t-distribution
  • Beta Distribution

2 Readings

  • Reference
  • Reference

Maximum Likelihood Estimation

    • 2 Videos
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2 Videos

  • MLE Estimation using a Beta Distribution
  • Gaussian Mixture Model

Non-parametric Methods

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Non-parametric methods

1 Videos

  • Non-parametric Methods: Kernel Density Estimation

Sampling Algorithms

    • 4 Videos
    • 2 Assignment
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2 Assignment

  • Sampling algorithms
  • Rejection and Importance Sampling

4 Videos

  • Introduction to Sampling
  • The Inverse Transform Algorithm
  • Rejection Sampling
  • Importance Sampling

Bayesian vs. Frequentist Algorithms

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Bayesian vs. Frequentist Inference

2 Videos

  • Differences between the Bayesian and the Frequentist
  • Features of Bayesian and Frequentist Inference

2 Readings

  • Reference
  • Bayesian vs. Frequentist Inference

Auto Summary

"Introduction to Bayesian Statistics" is a foundational course in maths and statistics designed for aspiring data scientists. Taught by Dr. Srijith Rajamohan and Dr. Robert Settlage, it covers probability basics, Bayesian modeling, and inference using Python and Jupyter notebooks. The course spans 780 hours and is part of a three-course specialization, available on Coursera with a Starter subscription.

Dr. Srijith Rajamohan
Instructor

Dr. Srijith Rajamohan