Databricks - Introduction to Bayesian Statistics
- Offered byCoursera
- Public/Government Institute
Introduction to Bayesian Statistics at Coursera Overview
Duration | 13 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Bayesian Statistics at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 3 in the Introduction to Computational Statistics for Data Scientists Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level Some experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn. Knowledge of Jupyter Notebooks will be beneficial.
- Approx. 13 hours to complete
- English Subtitles: English
Introduction to Bayesian Statistics at Coursera Course details
- 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.
Introduction to Bayesian Statistics at Coursera Curriculum
Environment Setup
Welcome to the Specialization!
Welcome to Course 1!
Python Environment Setup
Introduction to the Databricks Ecosystem for Data Science
What can you expect from this course/specialization?
Introduction to the Fundamentals of Probability
Introductions
Chance regularities and random processes
Outcomes, events and spaces
Addition rules of probability
Multiplication rules of probability
Conditional probability, Random Variables and Experiments
Random Variables and Distributions
Moments, mean and variance
Joint distributions of Random Variables
Estimation using MoM and MLE
Basics of Bayes' Rule
Decisions and Loss Functions
Priors introduction
Priors as conjugates
Informative vs non-informative priors
Jeffrey's Prior
Prior distributions and posterior ramifications
Introduction and references
Rules for manipulating probability
Random variables
MoM and MLE
Bayes' and decisions
Loss functions
More on priors
Week 2 Belief and Probability Practice
Week 2 Belief and Probability Graded Quiz
Week 2 Manipulating Probability Practice
Week 2 Manipulating Probability Graded Quiz
Week 2 Distributions Practice
Week 2 Distributions Graded Quiz
Week 2 Estimation Practice
Week 2 Estimation Graded Quiz
Week 2 Decisions Practice
Week 2 Decisions Graded Quiz
Week 2 Priors Practice
Week 2 Priors Graded Quiz
A Hands-On Introduction to Common Distributions
The Binomial Distribution
Negative Binomial Distribution
Poisson Distribution
Exponential Distribution
Gamma Distribution
Normal Distribution
Lognormal Distribution
Student's t-distribution
Beta Distribution
MLE Estimation using a Beta Distribution
Gaussian Mixture Model
Non-parametric Methods: Kernel Density Estimation
Reference
Reference
Common distributions
Non-parametric methods
Sampling Algorithms
Introduction to Sampling
The Inverse Transform Algorithm
Rejection Sampling
Importance Sampling
Differences between the Bayesian and the Frequentist
Features of Bayesian and Frequentist Inference
Reference
Bayesian vs. Frequentist Inference
Sampling algorithms
Rejection and Importance Sampling
Bayesian vs. Frequentist Inference
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