Learner Reviews & Feedback for Bayesian Statistics: From Concept to Data Analysis by University of California, Santa Cruz
About the Course
Top reviews
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
426 - 450 of 687 Reviews for Bayesian Statistics: From Concept to Data Analysis
By Ankur S
•Very good course
By Justin C
•Excellent Course
By Gaurav a
•Very encouraging
By Martin K
•Best course yet!
By Andrei M S
•Learned a lot.
By Jakob R
•Great course!
By 조휘용
•good course!
By Efren S
•Great stuff!
By FNU R M
•Nice Course
By Binghao L
•nice course
By Joshua M
•Good course
By Zito R
•Excellent!
By Rigoberto J M A
•Excellent.
By Vinicius P d A
•Very good!
By Hortensia M
•excelent!
By FERDINANTOS K
•THANK YOU
By Benjamin S K
•recommend
By How
•Completed
By Jinxiao Z
•excellent
By shashi r
•Awesome.
By Xinyi J
•Great!
By Anna B R
•Great!
By Wai Y L
•Good!
By Benjamin A A
•j
By Artem B
•This is a great course and I have learned a lot. The teacher is extremely knowledgeable and formulates things very clearly. However, this is really a math course. For me it was hard to stay motivated because the language of the course is mathematics, the teacher juggles with the concepts that my mind was still trying to process and absorb. I was able to finish all exercises, including the honors ones, but when I finished the week 3, I had to redo it completely again and buy a book on Bayesian statistics by John Kruschke which helped me immensely to rethink the basic concepts again. This course could be excellent if it included more reiterations of concepts, was explained in more general language, the pace was slower and most importantly included more practical applications. The typical statistical examples of coin flipping are fun, but too abstract. In the end, I want to know how I can apply Bayesian statistics. A lot of knowledge of mathematics was assumed and I had to look up a lot of concepts myself. The derivations sometimes also went too quick and supplementary materials were quite dense. I think this course is a perfect refresher course for someone who has mathematical background and has taken a Bayesian statistics course some time ago. But for the beginner with some mathematical background (I am familiar with the frequentist statistics, machine learning, calculus) it was too much of a challenge. If it were not a Coursera course, where I can rewind endlessly and work at my own pace, but a regular university course, there will be p=.9 that I would drop out, while my prior for dropping out would be p=.05
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