Sep 20, 2017
Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.
Apr 9, 2018
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
创建者 Erik F•
Jun 19, 2017
Unlike the previous sections in this specialization, this one has no reading material, nor does it have many problem sets to solve. You will definitely need to find external resources in order to complete this section, because numerous concepts are glossed over, explained vaguely, or explained poorly. I recommend Kruschke's "Doing Bayesian Data Analysis" as a very accessible way to learn Bayesian statistics. I'd have no confidence using Bayesian approaches in practice from only the material taught in this section. Frankly, this section seems like it was hastily thrown together, and I was very disappointed.
创建者 Eszter A•
Sep 13, 2016
This course needs much more work from instructors before it gets offered to the public. It is poorly assembled, offers hardly comprehensible material with no or very few resources to turn to. Reading material is listed, but they are useful for people already skilled in Bayesian Statistics. Exercises are worded such, that even the questions are a challenge to understand. Quizzes contain material never mentioned during lessons. Discussion forums are left unanswered by the teaching staff - or if they reply, they do it in a very negligent manner. No support on the merits. A major disappointment.
创建者 Graham G•
Oct 1, 2019
This course is awful, especially compared with the rest of the courses in the specialization. I had to read an entire Bayesian statistics text book in order to understand this area, and this courses still made little sense. This specialization is supposed to be for beginners and yet this course gets into intense mathematical notation with no preparation or guidance. I have somewhat of a math background, and this course was not only extremely difficult to finish, I don't feel like I really learned much of anything at the end. This course needs to be redesigned from the ground up.
创建者 Paul G•
Dec 27, 2020
While I have taught basic statistics courses and have a PhD, I have no prior background in Bayesian Statistics. The coverage of Bayesian concepts lacks sufficient depth for a novice in Bayesian statistics and the materials provided do not provide any further depth. None of the texts I have on hand cover Bayesian statistics at all. The focus of the specialization is supposed to be on learning R as applied to statistics. Between the unfamiliarity of Bayesian statistics and the use of an experimental version of a function in Week 3, I learned essentially nothing about using R.
创建者 Marina C R•
Jul 31, 2017
Unlike the first 3 courses of this specialization, which were excellent, this one is not recommendable at all. As many other students have reported, the teaching material is not enough neither to understand the subject nor to do the graded material. I am really disappointed because the problem seems to come at least 4 months ago but the teacher (which by the way is far to be as good as Mine) has not replied. Instead, mentors have suggested to use the forums to make questions but it is neither affordable nor acceptable.
创建者 Renat M•
Sep 8, 2017
The course is too sketchy: it does not provide enough materials to grasp the main ideas of Bayesian Statistics nor it gives any details about some formulas and important principles.
This course does not have a book to follow along as the previous courses had (statistics).
I had to spend more than 2 months to be able to understand all the concepts that this course was trying to teach. In this sense watching Youtube videos and reading papers was much more helpful than the entire course itself.
创建者 Cindy C•
Feb 5, 2017
This class assumes a lot of statistical knowledge and background that is not covered in the first three classes of the series. So much statistical terminology and jargon was used by the instructor, it felt like taking a class in another language where I had to constantly stop the video and google for the terminology she used. It took a lot of grit to finish the class, which was overall a very demoralizing and negative experience.
创建者 Santiago R•
Sep 16, 2020
The material has not enough contextualization. The explanations are way to superficial. Its not necessary to explain everything, but even the intuition is lost. The teachers dont help: except from Çetinkaya-Rundel the others read from a telemprompter and one even has to wonder if they know what theyre saying. It seems that theyre more worried to dont loose the pace of the teleprompter than to convey meaning.
创建者 Ilya P•
Sep 13, 2017
While the first 3 courses had ample examples, guided practices, and other tools to learn, this course does not. Quizzes do not have good explanations, and videos do not have guided practice. There is no book to follow, hence, learning the material is difficult.
Instructors need to rework the course to include books, guided practices, and guided R examples in order to aid comprehension.
创建者 Ben R•
Apr 8, 2018
A frustrating course, especially when compared to the other courses in this specialization. Lectures alternated between over my head and not giving enough information. Projects seemed designed for someone with a better grasp of R. I will probably look for another course on Bayesian statistics, because I feel my grasp of these concepts is still weak.
创建者 Michael F•
Sep 21, 2020
The information felt purely academic. I know we were show how professionals have used this type of analysis before, but those examples were way more advanced than the scope of this course. Moreover, the scope of the course was too broad. More information on how to model non-linear data would have been more valuable than this.
创建者 Andrew B O•
Aug 11, 2017
The change of instructors negatively affected this class. The new instructors are nowhere near as good at explaining the data and tending to start talking about things without even explaining what they where to to use a lot of activations, which one would need to continually look up.
创建者 Naren T•
Dec 26, 2019
Very poor explanation in week 3, the new professor is not explaining the definitions or the use of them properly. Too many jargons.
Professor doesnt explain the use of prior predictive distribution and just introduces the formula without any consideration for explanation
创建者 Yu-Chi B•
Oct 12, 2020
No efforts on maintaining the quality of assignment. You will be hard or never to finish them.
Too much information concentrated in one course without clear elaboration. It should be separated to 2~3 courses.
创建者 QIAN Y•
Jul 29, 2016
The course lacks of explanation and it's very difficult to follow. It seems that the instructor just reads the slides without reasoning and explanation. Suggested reading materials are needed.
Jun 30, 2019
A huge leap from the other courses in the specialization, which are all extremely well-constructed. Terms are not introduced and explained properly, and the whole course seems very haphazard.
创建者 Cosma A•
Feb 15, 2018
1St problem speed of teaching, also other students complained
2With such a speed, material was too condensed for such a broad subject
3Not sufficient explanations for a statistics beginner
创建者 Tom D•
Aug 5, 2016
This course is not well-presented. Lectures are unimaginative, and there isn't enough supporting material or readings.
创建者 Paul J•
Jul 2, 2017
Quizzes are not related to videos. There is very limited practice problems (the best way to learn math subjects).
创建者 Chen Z•
Oct 26, 2016
I get really frustrated when the tutor doesn't explain lots of concept/symbols in the materials.....
创建者 Ashish C•
Aug 29, 2019
The quality of teaching was drastically down as compared to other courses.
创建者 Jeffrey W•
Jun 2, 2018
Unclear information, too vague, incomplete presentation of ideas.
创建者 Jose C G•
Dec 5, 2022
It is a pity that the course is for R
创建者 Shubham J•
Sep 15, 2019
becomes too much confusing at times.