Back to Bayesian Statistics: From Concept to Data Analysis

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3,052 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

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By Brandon H

â€¢Mar 6, 2018

This is a great course! Much better (and cheaper) than the course I took in grad school. Full of practical knowledge, and isn't too overwhelming on the mathematics/statistical theory. It's just right. Good for anyone interested in Bayesian statistics, though some background with probability distributions may help climb the learning curve.

By Andres F

â€¢Jul 11, 2022

Excellent course.

It provides a strong basis for understanding the topics and many examples to see applications.

The topics are introduced gradually, and the quizzes are well organized to provide a step-by-step understanding.

I particularly enjoyed the conceptual and intuitive discussions of all major topics provided by the instructor.

By Natasha

â€¢Dec 27, 2016

I really enjoyed this course. The lectures were short and clearly explained, and particularly highlighted why Bayesian statistics is different and what is useful about it. I would have like a bit more walk-through on some of the derivations in weeks 3 and 4. More R exercises and further resource recommendations would have been useful.

By Galley D

â€¢Sep 11, 2017

Outstanding course to understand Bayesian statistics. Teacher is very pedagogical and the course delivery with equations written on the transparent board make everything easy to follow.

As an area for development, I would have like more information on Bayesian linear regression in week 4, through background lecture or dedicated video.

By Ayobami A

â€¢Jul 19, 2020

This was a great course! I had NO background in Bayesian statistics other than knowing the Baye's theorem but was able to get through and pass all the quizzes. You need to have some knowledge of probability theory though.mWatching and listening keenly to the videos, and going through the supplemental materials was extremely helpful.

By Fedor T

â€¢Jan 21, 2017

Very clear lectures masterfully delivered by prof. Lee. The quizzes are good, if somewhat on the easy side. Don't be discouraged by the choice of R as the tool for assignments. R is flawed as a programming language, but you won't need to do any programming, only one-liners to evaluate various statistical functions and plot results.

By Nathaniel R

â€¢Nov 21, 2016

This is the first online course I have ever taken so I don't have anything to compare it to, but this course was excellent! The lectures and materials were very clear and I will be adopting some of Prof. Lee's approach into my own teaching practice. The bar has been set very high for any future online courses that I will take!

By Thomas G

â€¢Mar 5, 2022

Herbert Lee provides a sound introduction to Bayesian statistics while also offering, to the attentive learner, an analysis of the frequentist paradigm (e.g. the pitfalls of making objectivity assumptions and using p-values). The course requires less math background from the learner than it helps building. Highly recommended!

By Musa J

â€¢Aug 11, 2017

Herbert Lee's Tests are fun (Best!) to learn during the test! Lectures are succinct; Format of writing on the glass towards you and then flipped was right & original. Went on to try Kaggle problems independently. For usable feedback need tiny bit more on Poisson, Gamma, non conjugate intuitively & darker shirts as background.

By Labmem

â€¢Sep 11, 2016

Good course. This course is quite challenging for people who don't major in math or physics. However, it isn't so difficult to understand as the post half of this course has a lot in common. In my experience, understanding the concept of priors and posterior estimation is the core of this course. Have fun learning this course.

By Alex T

â€¢Mar 2, 2023

It's a great intro course into Bayesian statistics.

I'm a computer scientist, and data science was my lifelong professional interest. I always wanted to learn more about the Bayesian statistics, and this is an excellent course. It's mathematically rigorous - but not too hard. And the instructor is doing an excellent job.

By Stephanie N

â€¢Jan 14, 2023

Provided a nice balance of theory and hands-on examples. Also a good balance of hard enough that I couldn't just casually answer the questions without really working through them and understanding the concepts, but not too hard as to make me feel I couldn't squeeze this into my schedule here and there. Great foundation.

By Victor A

â€¢Mar 1, 2017

It's a great course, there is a lot of information and it might seem at times overwhelming, but it's organized nicely and prof. Lee has a very comfortable time explaining all the concepts. A few more examples would have made this course easier, but that does not mean it would have been better. It's as good as it gets

By Theofilus H P

â€¢Aug 23, 2020

This course offers great explanations about Bayesian statistics. Although the course is a bit hard, by understanding each example provided in each lecture, I was able to grasp the basic concepts and ideas about Bayesian statistics. Also, I am now able to use R for Bayesian statistics thanks to this course.

By Kevin L

â€¢May 24, 2020

This is a great course for anyone with no prior knowledge of Bayesian statistics. The instructor did a great job explaining the concepts and provided good examples. I also liked the quizzes and activities in R/Excel. I learned a lot from this course! I plan to take a few more courses in Bayesian stats.

By John G

â€¢Oct 30, 2017

Prof Lee derived the formulas in an upbeat way, which helped me learn. I'd suggest putting the actual lectures into pdf for later reference, like is done for supplementary material. Homework assignments were challenging and educational. You might suggest a review of prob distributions as pre-requisite.

By William P

â€¢Aug 3, 2018

Fantastic first course. The only concern I have is with the software choices. I have neither R nor Excel, but was able to easily use google Sheets. It might be worth mentioning to students that this is an option. There is even a stats package that claims parity with one of the listed packages for excel.

By JosÃ© R

â€¢Aug 23, 2020

The quizzes in the course are very well elaborated and designed to help you learn points and details not explicitly stated in the lectures. The contents and pacing are just about right for me. Perhaps the section on normal inference would need more elaborated as this part was the most difficult for me.

By Najib B

â€¢Aug 26, 2021

This course provide the theoretical basics for anyone who wants to understand, and hopefully work with, Bayesian stats. Prof. Lee's exposition of the math behind Bayesian stat is precise concise and to the point. If I, with only high school math from age ago, could understand, I believe anyone can.

By Guido W R

â€¢Oct 5, 2016

Very nice course that in my opinion nicely fits between Bolstad and Gelman in difficulty (talking in popular Bayesian Data Analysis books). Herbert Lee does a very good job at building one's intuition and understanding in the general Bayesian inference. Good starting point for moving on with Bayes.

By OanÃ d S d C

â€¢Apr 21, 2018

Amazing. Simple, fast, dense, very well taught. I loved the professor, his commentaries and way to explain the contents. Thought the exercises were OK, maybe simpler than I taught but the comments in them helped me a lot to understand the topics. 10/10, a new and better way to teach! Very useful.

By Erick O

â€¢Sep 27, 2020

Un curso muy bueno, sobre un enfoque de la estadÃstica que desconocÃa. AdemÃ¡s de reforzar muy bien las cosas que ya sabÃa y darles ese enfoque Bayesiano. Me gusta que todo se resume en la importancia de la probabilidad condicionada, ya que el teorema de Bayes conjuga las probabilidades inversas.

By Derek H

â€¢Jun 12, 2019

Good to learn or re-learn the basics of statistic and probability, and as a foundation for learning maximum likelihood methods (which are much more useful later on). The material is digestible, to the point, and the quizzes are helpful in checking your understanding and information retention.

By Tapan K

â€¢Jan 10, 2022

This is an absolutely fantastic course for anyone interested in Bayesian Statistics. It is certainly not an easy course to cruise through and I highly recommend thoroughly experimenting with the concepts taught in the videos. I had a great time learning from Prof. Herbert Lee, he is amazing!

By Devesh S

â€¢Jun 30, 2017

A well organized course, learned important concepts in statistics and probability that will definitely help anyone wanting to specialize in machine learning or take up data science. Clear and concise explanation of theory focusing on application that is adequately tested in the exams.

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