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學生對 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: From Concept to Data Analysis 的評價和反饋

2,535 個評分
667 條評論


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....



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.


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


576 - Bayesian Statistics: From Concept to Data Analysis 的 600 個評論(共 654 個)

創建者 Aravind M


Good introductory course. Could provide more hands-on examples

創建者 Juan C C E


Explain with more details the concepts, the mathematics is ok

創建者 Dziem N


I wish there are lecture notes to accompany the videos.

創建者 Wate S


For me Chinese, it 's not easy to understand the quiz.

創建者 Vittorino M C


Well explained, I fit all the gaps about probability.

創建者 Gil S


Clear and consise introduction to Bayesian statistics

創建者 Yuanruo L


Good and simple introduction for Bayesian statistics.

創建者 Sunsik K


well instructed basic course of Bayesian statistics.

創建者 Alexei M


More examples are required as well as more practice

創建者 Venkataraghavan P K


Loved the theory & analytical part of the course.

創建者 Bishal L


It is a nice introductory course on Baysian s

創建者 JhZhang



創建者 Carson M


Pretty good overview of Bayesian statistics.

創建者 xuening


from week 3, the learning curve become steep

創建者 Wenbin M


The normal distribution part lacks detail.

創建者 Ezra K


Good overview of Bayesian statistics.

創建者 Xindie H


Nice and easy introduction course.

創建者 Witold W


Liked it and can recommend it.

創建者 Chuck M


A good course - recommended.

創建者 Valentina D M


Need more material on R.

創建者 Ankit P


Excellent fundamentals.

創建者 Spyros L


Very good introduction!

創建者 Guim G P


Very useful!

創建者 kaushal k S



創建者 Linda S


In the course, I liked that there were questions asked during the videos. That makes you think about the content, the professor was just talking about.

Anyway from my point of view, the supplementary material should have covered more of the content of the course. That would have helped me a lot.

Also, I sometimes felt lost when the video started, some introducing words why this topic is now discussed, or an overview about the topics handled in the topic area would have helped me to understand the connections. What would have also helped are overview slides (also in the supplementary material e.g.) Also I had sometimes the feeling, that the answers to the questions of the quizzes were not always included in the videos. For this, I would have been glad to have a extensive supplementary material.

To sum up, I was able to learn a lot, but I could have learnd a lot more with better supplementary material or a clearer structure.