- All DegreesExplore Bachelor’s & Master’s degrees
- Computer Science & EngineeringExplore Computer Science & Engineering degrees
- BusinessExplore MBA & Business degrees
- Bachelor’s DegreesExplore master’s degrees from leading universities
- MasterTrack™Earn credit towards a Master’s degree
- University CertificatesAdvance your career with graduate-level learning

Back to Bayesian Statistics: From Concept to Data Analysis

stars

3,050 ratings

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses....

GS

Aug 31, 2017

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.

JB

Oct 16, 2020

An excellent course with some good hands on exercises in both R and excel. Not for the faint of heart mathematically speaking, assumes a competent understanding of statistics and probability going in

Filter by:

By Jens R

•Jan 31, 2017

It was pretty intuitive and easy to follow the first couple of weeks, but then the assumed knowledge of beta and gamma distributions and their frequentist usage, stood in the way of me fully grasping the Bayesian part of it. In the end I just copied the examples from the lectures and passed the tests ... without really getting it.

By Edoardo C

•Apr 20, 2020

Overall I liked the course but I would have preferred a more formal treatment in many cases - sometimes numbers were plugged into the formulas without first explaining their formal structure more in detail.

I did not like also the fact that the course was implemented in R and Excel (but that's a matter of taste of course).

By Deleted A

•Jun 9, 2020

I didn't think the lectures were very good. The instructor wasn't careful with his notation, which was very confusing, and the initial lectures where he used a pastel green marker on a green background and wearing a pastel green shirt made his blackboard text nearly invisible.

However, the assignments were execellent.

By Dmitry S

•Sep 20, 2016

The material is good, but I've found the lectures challenging to understand even having some background in math. It would be good if all the definitions and key facts were stated more prominently in the lectures, as opposed to algebraic transformations which most readers can hopefully do on their own.

By Ahmed S

•Jan 4, 2017

This course requires solid grounding in mathematics. No meant of Social Science graduates without proper training in statistics/mathematics. The course was good in the sense that we could how probability distributions are used to model real world problems.

Study material was certainly not adequate.

By Ray P

•Jan 14, 2022

The course presented fundamental concepts of probability, regression, and Bayesian ways of thinking. However, it lacked in applications of Bayesian approaches beyond the most basic. For example, how do we estimate these models on real data to obtain parameters and make inferences or predictions?

By Yuzhong W

•Oct 3, 2016

The lectures from week 1 to week 3 are nice and useful to me, but I think there should be more details about the content in week 4. For example, I think the lecture about the Jeffreys prior skipped many things and I did not understand this concept very well.

By Damel L

•Nov 29, 2019

Most of the support material should be prior reading. Lecturing could be more useful i.e. explaining ore about why we use certain distribution and how to apply them. Most of it as just reciting formulas and felt like a waste of time...

By Olexandr L

•Jul 1, 2017

It was quite difficult to learn from just the material provided here, and I had to look for info on the web. Also, adding modern real life examples and going into detail would make this course better

By Jesús R S

•Jul 19, 2017

Good course as an introduction to bayesian statistics if you want to pursue more advanced courses in the field or to get some practise working with distributions under the bayesian framework.

By Silvia Z

•May 8, 2020

In general, the course is useful, but in half of videos the explanation focused mostly on formulas, and less on theory. I personally had difficulty in learning theory of Bayesian statistics.

By Borja R S

•Apr 25, 2020

The teachers are clearly experts in what they do, but sometimes I think it is that same expertise that makes them jump to conclusions too easily, making it difficult for beginners to follow.

By 危然

•Jul 25, 2020

This course gives a very brief background on conjugate prior. However, the lectures on Bayesian linear regression is too superficial. I wish the lectures could have gone into more detail.

By Carlos

•Apr 8, 2020

Too much time spent on the beginning and too little on later more complicated concepts such as the posterior predictive. It felt as if that was just a side note in the extra readings.

By Augusto S P

•Sep 24, 2017

The course is good for beginners in statistics. In my opinion it would be better to invest more time explaining different topics about bayesian regression and bayesian time series.

By Oliver B

•Jun 1, 2020

Solid mathematical grounding, but would have benefited from more time spent on the history of Bayesian inference, when to use it, why it can be used etc..

By Pranav H

•Jul 1, 2018

The course could have given more information on tiny details which can confuse people during the exercises. But overall a good learning experience

By Ángel L

•Jul 4, 2021

It’s ok to have a theoretical basis about Bayesian Statistics, but I missed some practical cases using Python instead of R. I also missed PYMC3

By Kathryn L

•Jul 23, 2021

It's a nice introduction to the topic, but I often found the lectures to be imprecise or inconsistent, especially with respect to terminology.

By Alessandra T

•Jun 29, 2017

We still don't understand how Bayes differs to Frequentist... A worked example comparing the two at the end would have been nice.

By Ken M

•May 1, 2019

It would have been great if more graphs had been provided, for easier visualization of the e.g. distributions, or concepts.

By roger

•Jul 24, 2019

It would be better to add more explain about those equations and connect the math stuffs with the real world samples

By Max H

•Jul 14, 2019

It would be much better if there was a more sufficient introduction to the various distributions used in the course.

By Victor D

•Jul 9, 2019

Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.

By Isra

•May 4, 2020

Good course!!... Additional examples of real life explained and done in R or excel will make it great

- AWS Cloud A Practitioner's Guide
- Basics of Computer Programming with Python
- Beginners Python Programming in IT
- Developing Professional High Fidelity Designs and Prototypes
- Get Google CBRS-CPI Certified
- Introduction to MATLAB Programming
- Learn HTML and CSS for Building Modern Web Pages
- Learn the Basics of Agile with Atlassian JIRA
- Managing IT Infrastructure Services
- Mastering the Fundamentals of IT Support

- Basics of Computer Programming with Python
- Beginners Python Programming in IT
- Building a Modern Computer System from the Ground Up
- Getting Started with Google Cloud Fundamentals
- Introduction to Cryptography
- Introduction to Programming and Web Development
- Introduction to UX Design
- Learn HTML and CSS for Building Modern Web Pages
- Mastering the Fundamentals of IT Support
- Utilizing SLOs & SLIs to Measure Site Reliability

- Building an Agile and Value-Driven Product Backlog
- Foundations of Financial Markets & Behavioral Finance
- Getting Started with Construction Project Management
- Getting Started With Google Sheets
- Introduction to AI for Non-Technical People
- Learn the Basics of SEO and Improve Your Website's Rankings
- Mastering Business Writing
- Mastering the Art of Effective Public Speaking
- Social Media Content Creation & Management
- Understanding Financial Statements & Disclosures