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Avis et commentaires pour d'étudiants pour Statistiques déductives par Université Duke

4.8
étoiles
2,422 évaluations

À propos du cours

This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data...

Meilleurs avis

MN

28 févr. 2017

Great course. If you put in a little effort, you will come out with a lot of new knowledge. I recommend using the book after you have seen the movies. It gives a deeper picture of how it works. Great!

ZC

23 août 2017

This course by Professor Çetinkaya-Rundel is awesome because it is taught in a very clear and vivid way. Lab section and forum are so dope that I love them so much! Definitely strong recommendation!!!

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26 - 50 sur 434 Avis pour Statistiques déductives

par Liu X

23 oct. 2021

very helpful and easy to learn

par Gökhan G

28 mai 2021

It's a great course!

par checkie f r

19 nov. 2021

​thank you

par Boxuan L

1 nov. 2021

Thank you!

par Iuri B T

3 juil. 2021

Top

par Natalie R

21 mai 2019

Well-taught, but they need to provide more resources to help people learn R. R is not a user-friendly app and I needed to google how to do a lot of the things they're asking us to do. Needless to say, I can google how to work in R on my own without paying Coursera a fee.

par Maria T

3 janv. 2021

The course factually do not teach r. It's much longer than it was declared. The core statistical course is quite good, but information about computer simulation methods looks like was entered retroactively

par Mani G

8 juin 2017

some topics require more explanation!

par Garrett C

9 oct. 2021

This is an interesting course, but the textbook it uses is outdated by a newer edition, and classes / readings should be adjusted to reference the latest publication. The professor's videos explain statistics very well, but there was really very minimal discussion of R and R Studio in the videos themselves. The weekly labs went over a few R-based scenarios, but did not provide enough examples or a broad enough overview to prepare students to use the program effectively if they were not already familiar with the program. I struggled to try to use R to answer some questions posed by the labs, and there was no real support in the discussion forums to help me figure out where I was going wrong with my commands.

par Tate R

29 mai 2021

​I think that as a stand alone course, if R is going to be tested it probably needs a R teaching module. At the beginning of the course definitions and key words need to be identified better, in weeks 3/4 this was dine very well. But should be front loaded for obvious reasons. I also think utilizing one statistic set or "situation" frequently would be better when possible so thay learners can understand how the numbers and concepts are changing. But overall very effective course!

par Stefan G

3 juil. 2021

The course is interesting, the instructor competent and you can learn a lot when you also buy the book. Unfortunately the course is a few years old and the materials have not been updated since then. Code samples don't work anymore, links for material or quiz don't exist, references to the book are outdated (sometimes a wrong page, sometimes a wrong version), questions in forum are not more answered, and so on. If the course would be updated, i would rate it 5 stars.

par Aydar A

3 nov. 2017

It was good. But I feel like I've spent half of the time untangling sly phrasing of questions.

par Jamison T

5 juil. 2018

I should not be charged if I have completed the project and simply waiting for other users to review it. This is dependent on how many users are taking the course at any given time. A bad system that results in users paying more for uncontrollable uncertain factors...

par Danielle B

14 févr. 2021

As with the previous course in this specialization, the statistics instruction is quite good. The R instruction is almost non-existent. You can expect to spend plenty of time on your own, Googling the skills you'll need to complete your end project.

par Piotr Z

7 juin 2020

The course was not very helpful for me, as practical cases with R were poorly developed and the final data capstone project is badly formulated which makes it extremely difficult to pass.

par R. R

9 sept. 2020

Lots of perplexing questions in their assignments, and the quizzes were too difficult. However, I garnered modest skills in this program.

par THEJUS S

29 août 2020

Such a worst course never seen in my life

par Rui Z

14 mai 2019

Professor Mine is terrific. I'm sure she has a great depth of knowledge and grateful that she's able to deliver her knowledge out to listeners. She uses meaningful examples all along the course, no dry pure mathematical cases at all. That helps a ton to digest concepts. And she constantly repeat some core concepts and how to interpret a statistic right. I didn't realize how important this was until I was challenged with questions, then I came back and hear again her interpretation, and the whole thing became clearer. She's one of the best professors I've ever listened to, and I've been through grad school, met so many professors.

The current mentor Rolf was great at supporting. He answers a lot of questions in the forum. He's very responsible and supportive. So if you're considering on taking this course, take it now as mentor will change!

I haven't finished the course yet, but the enrollment rate seems to be quite decent, so I wouldn't expect it to take too long to get final project reviewed and get certificate. I assume this is an important issue for any course takers.

The only downside is that there could be more R code teaching, especially on complicated simulations. That way it may be more friendly to R beginners. I know it's important to do research ourselves for codes, but beginners could lack of proper terminology or vision by nature to do the research on Google. Especially when I'm physically in the Main Land of China, where it takes some efforts to even get on Google, so doing code research took a lot of my time and was a little frustrating towards the end.

But again, the overall course and support are great! If this is not a 5 start course, I can hardly give out my highest mark to any other courses. It helped me to understand inferencial statistics, practice R, and think more like a statistician.

par Hao C

6 nov. 2019

Teaching: I really like the clear and concise teaching style of lecturer and the wide range of simple real-life example used to explain the course content.

I’m a social science student. Although I’ve studied quantitative research methods before, this course gives me some new insights into inferential statistics. I think I will never forget the statistical meaning of p-value after this course!

Course Structure: The course structure is well organized with clear focus in each week.

The first and second weeks are easy to follow, but the third and fourth weeks are more challenging.

Textbook: The textbook used in this course is a good supplementary material, although it is not necessary to read the textbook. Course videos have already explained everything that we need to know at intro level. However, it is worth reading the textbook for the third and fourth weeks.

Assessment: The assessment of quiz in each week is relatively easy. The exploratory data analysis required in peer-reviewed assignment is slightly challenging, because it might be hard for beginners to touch every required point.

par Shobhit K

16 mai 2021

Thank you so much! I learnt what I came to learn. Well curated and organized. Explores all potential areas of statistical inference and hypothesis testing and gives you a framework to relate between the two. Only feedback is to have one final conclusion/summary video where you can summarize how to use everything that we have learned based on the type of variables and the question at hand, i.e. mapping the learned approaches to types of problems. It was available as a scoring criteria in reviewing peer assignments but would be great to have it as a separate concluding video/note as well.

par Tanika M

12 juin 2020

I feel like I gained a solid foundation on inferential statistics from this course. I found the videos useful, with many well-explained examples. I would say that the readings should not be treated as optional - they were where I did the bulk of my learning. The scope of the final project is reasonable.

It may be relevant that I also took the previous course in this specialization, and I can see that not having done the previous labs would have made the project a little harder. As other reviews have said, the forums also seem much less moderated than the last course's.

par Jorge L

19 oct. 2016

Terrific course, i got here after starting the Data Science specialization on John Hopkins uni on Coursera, but there bit on statistics is awful, a waste of time.

I decided to give Courser another shot and definitely not regretting it, this course really go over the basics clearly and make sure to make enough exercise to revisit that clearly explain the fundamentals.

I was happy as getting to the final assignment i found myself doing quite an advance analysis and inference that i notice i really understood the topics on the course.

par Elaina K

19 mai 2022

This course was more intense than the first one in the Statistics with R specialization. Lessons are logically organized and well constructed. Instruction was generally clear. I would have preferred submission of the project with peer evaluation as opposed to self evaluation only, although the instructions provided were clear and well written. Going through all the material (listening to lectures, working exercises, quizzes, and project) takes much more time than advertised, and is well worth the time investment though.

par 이제민

6 août 2016

It has a little expensive tuition fee than other courses such as Data Science (Johns Hopkins) and Data life (HarvardX_edx). But I decided this course rather than choosing the others because I felt that it was well organized and quite good supplements. What I like most about this course is instructor. She looks like enthusiastic to give a her idea and wisdom. It attracts me to take this course even though it is expensive relatively. Anyway, I appreciate her for dedicated teaching in advance.

par Kuntal G

6 oct. 2016

It is really the best Statistics course that i have ever done. After doing all the course in statistics i'm very much confident in statistics. The course and Specialization is very clear, concise, nice explanation with example videos to have better understanding of the theory. It is highly recommended course for anyone interested to learn statistics in their career. Please do the maximum the course in the specialization to have good grasp of statistics if you are beginner like me :)