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Voltar para Bayesian Statistics: From Concept to Data Analysis

Comentários e feedback de alunos de Bayesian Statistics: From Concept to Data Analysis da instituição Universidade da Califórnia, Santa Cruz

4.6
estrelas
2,933 classificações
762 avaliações

Sobre o curso

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....

Melhores avaliações

GS

31 de ago de 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

16 de out de 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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526 — 550 de 755 Avaliações para o Bayesian Statistics: From Concept to Data Analysis

por Thomas F

28 de jun de 2017

Very good course, I may have been at a bit of a disadvantage because I came from a behavioural sciences background rather than a full statistics or math background. It was interesting though, and I think I acquired the requisite skills to conduct a Bayesian analysis in future. However, at some points in the class it does become very formula heavy, which I did find tough to grasp at some points.

por Arasch M

7 de jul de 2019

The course helps in developing a quite sound grasp of the Bayesian approach to the world. The assignments are feasible and help in gaining a deeper understanding of each subject. However there is a caveat: You definitely need to review your math skills before starting this course (esp. calculus, arithmetics and combinatorics) otherwise you'll be struggling with the particularities !

por Joshua A

3 de set de 2017

Excellent introduction to Bayesian statistics. More proofs would have been nice (perhaps an optional advanced material section?). The later half of the course increases quite a bit in difficulty and could use 1-2 more examples + applications. Professor did a great job and the quizzes thoroughly tested my knowledge. Overall, I would definitely recommend this course.

por Diogo P

19 de jul de 2017

Great lectures. The explanation of each topic is extremely clear and avoids excessive mathematical burden. Lectures are short and concise. Quizzes or at least Module Honors could be a bit more challenging, though. It's a great course, anyway. I'll be looking forward to enroll in the next course of the sequence, entitled "Bayesian Statistics: Techniques and Models".

por Francisco A d A e L

30 de nov de 2016

Very good course, with less emphasis in the videos and more on exercises and critical thinking, the way I like and learn the best. I particularly liked that the lecturer writes on a transparent vertical surface standing between him and the camera, very convenient. For those not so familiar with mathematics, this might hurt a bit but the payoff is super positive.

por Deleted A

30 de jul de 2019

Really enjoyed the course! Thank you. I would have given a higher rating if: 1) the instructor had spend more time on the intuition underpinning different derivations, 2) provided more context, 3) discussed more examples from practice. However, I am definitely continuing on to "Bayesian Statistics: Techniques and Models"! Thank you once more, team UCSC!

por Anderson F

13 de abr de 2020

I enjoyed the course. I was looking for a way to improve my knowledge of statistics and bayesian maths. I mainly used excel for the calculations. I would appreciate an additional tutorial on plotting mass and PDF function etc against Theta and real world variables to explore impact of parameters on distribution shape on prior and posterior results.

por Tim B

27 de mai de 2020

Exceptionally interesting class. Professor was knowledgeable and engaging. The key insight was to approach the "quizzes" as homework, a learning process. Some of the lectures were of variable audiovisual quality and the pacing of some sections was not uniform, but overall, a triumph. More from this professor please! Fun.

por PS

19 de mar de 2021

Good refresher course. Like a number of Coursera courses, it moves from basics through to more advanced topics quite quickly at times and necessarily skips over some of the more tedious but important distributional derivations. Would like to have seen more practical examples of Bayesian regression and its applications

por Florian M

2 de mar de 2018

Herbert Lee is great at explaining the mathematics behind Bayesian statistics. However, I think the course can improve greatly by also focusing more on context and the intuition behind the mathematics. I often found that I was able to pass all quizzes, while I did not 100% understand why I was doing what I was doing.

por JAY C

12 de jun de 2020

Great discussion into the ideas. The quizzes are relevant to the lectures as well and pretty straightforward, you don't need to go outside of the lecture itself to be able to do the quizzes. the only thing would be it would be good if the lectures notes were in print as Prof. Lee's writing is sometimes hard to read.

por Ali Z

22 de nov de 2016

As a grad student myself, I liked the way this course was presented in short video format and in only 4 weeks. Definitely there are much more to learn about Bayesian Statistics and one can go way deeper, but this course gives the required basic Bayesian knowledge to someone who wants to get familiar in a short time.

por GR P

15 de jun de 2021

A​n excellent course which focused on important concepts. I dont know if I could have done it without some background in probability. I would have liked more help with last honors quiz, which was frustrating. I wonder if coursera would include tutors that could be paid by learners to help?

por Aditya D

16 de jul de 2019

The course itself is well structured and covers a lot of material.

There are points in the course where the order of reading material and videos needs to be switched. Also, it would help to update some videos with a little more explanation. It appears as if the lecturer is skipping steps.

por Marc D

26 de jan de 2019

I liked it as introduction to Baysian statistics. With the material provided it was quite easily possible to answer the questions. I would have preferred that the videos of the course contained all the material and that it would not have been required to have read through material.

por Thierry C

30 de set de 2019

The course was well explained and there were several exercises pushing the learner to understand the logic behind the mathematical concept. I think it is a suitable class for people with already a certain level of statistics knowledge, even though all concepts are well explained.

por jose m

17 de abr de 2017

I think that, besides lesson 11 and 12, everything was very well explained. I was a bit confused with lessons 11 and 12 since I am not new to econometrics. Perhaps I found it confusing the theory background related to the lessons themselves. Just my opinion, very good course.

por Praveen K

1 de jun de 2020

The course was very well designed, I got to learn about a lot of new things in statistics that I had to understand. But for a Data Analyst working on large data sets and primarily working on ML this course is far too basic. Also, some of the concepts can be explained better.

por Rakulan S

25 de jul de 2021

V​ery concise and informative introduction to Bayesian statistics. Requires a fair bit of research besides just watching the course videos. But that only adds to the fun. Feel much more confident in my ability to estimate uncertainties in model parameters / predictions now.

por Łukasz F

5 de fev de 2019

I really liked the course.

What I think could be nice improvement would be more nsightful notes. Which means, that after every video, there should be a separate sheet with all the formulas being described in more detail, so that you can refer to them any time during quizes.

por Thomas J M

21 de mai de 2018

Overall the course is pretty good. They breakdown the concepts into clear and concise lectures. My only grip, is that the quizzes occur a little too frequently. They really interrupt the flow of the class. I would definitely prefer them spaced in 30-60 minute interval.

por Ekaterini T

31 de out de 2018

I found the need to search for most of the material needed to understand the lessons in other sources. Other than than it was a relatively easy class, which covers nearly the basics. This is not a tutorial on Data Analysis on R, although a short introduction is provided.

por Mohd S

18 de nov de 2019

Course covers the concept in a very simple way. Examples and assignments are very good.

However some of the statements made throughout the lectures needs more explanation , the course did not dedicate any videos to get familiar with terminology related to probability.

por Luiz G S S

17 de abr de 2020

It is a really interesting course. However, I think it should include more examples and meaningful ways to estimates some parameters. For example, how can I estimate alpha and beta for an Inverse-Gamma distribution in order to obtain a prior for the sigma-squared?

por h

14 de jan de 2017

Pen hard to see against shirt. Was mildly irritating to wait for prof to write out stuff, maybe prewrite it?

Went too fast forward for me, would've liked complementary optional material, eg extra quizzes, to help understand and get used to the tougher parts.