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

1,682 évaluations
307 avis

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


Aug 24, 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!!!


Mar 01, 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!

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251 - 275 sur 304 Avis pour Statistiques déductives

par Charles G

Jan 20, 2018


par Gonzalo C S

Jul 24, 2016


par Saravanan

Feb 01, 2019


par Radoslaw T

Mar 18, 2018


par Wu X

Apr 07, 2020

I gave this course 4 stars. The missing 1 star is because this course has no content about R (but it is in a specialization called "statistics and R"). This course is only about statistics and the videos and instructor is good. The instructor explained the complex concepts well. At the end of the course, you need to do a project with Rstudio. I had no idea how to clean and manipulate the dataset and I had to drop out this course for sometime and register an account in another online education platform for programming for R specifically and learn how to handle those string, manipulate the datagrams and tables and extract the data I need from a dataset with thousands of variables. And then I got back to this project with more confidence and finally finished that.

par Lucy M

May 22, 2020

Well structured course to take at your own pace. I did a stats course about 5 years ago and this has been a good refresher - not sure how hard it would be for a total novice - i think it would take more time than suggested. Warning, if like me you have prior experience in R the assignments will take a little more figuring out too. The discussion forums have most the answers and help you need and actually the peer-review is really helpful to 'learn by teaching'.

par Shahin A

Oct 01, 2016

Some parts are needed more clarification. In other words, as a student of the course you need to go beyond the materials, since the materials are not self-sufficient. Specially about simulation methods. However, this is not the reason that I give the course 4 out of 5. The absence of any help from TAs, based on my experience, is the reason. I expected some official replies to my question while there are only a few question for each week of the course.

par Janio A M

Jul 29, 2018

Great material although I will like to know more about the practical side of statistical inference. For instance, I have more of less an idea of how to use chi-squared test with categorical variables in a dataset however, for the other statistical inference methods such as p-values and confidence intervals I still don't see where can I use this methods when doing data analysis. Can we use this to detect outliers in our dataset for instance?

par Chutian Z

Apr 16, 2020

Better than the Basic Statistics offered by the University of Amsterdam. That course was too informal, didn't address the techniques and covered too few materials. I love the fact that there are accompanying R labs. However, the course should teach the students the more general R functions (qt,pt,qnorm,etc.) instead of the self-developed "inference" function. In addition, it's a little hasty in week 4. The pace should slow down.

par Amy W

Dec 12, 2019

The course is well designed, and the examples given in each lesson are informative and interesting.

For the final project, I wanted to group some categories from one variable together in a new variable, but I did not have the code I needed to do it. It would have been very helpful to have that information in one of the labs prior to doing the final project.

par Richard N B A

Jun 19, 2016

Thorough treatment of the topics with great examples using real data. On the down side, the treatment of the mathematics behind the formulas is a little light. Great use of simulation to support the theory or to use when theoretical assumptions are not met. Strongly recommended!

par Anna D

May 22, 2017

I loved this course. As with the previous course a lot of things that weren't clear to me before are now. I totally recommend it to anybody new to statistics or anybody who is struggling with statistics (like I have for a very long time).

par Robert S

Dec 27, 2017

Very good material which gives practical knowledge supported by interesting examples. The only concern is that it is slightly shallow - lacking some mathematical justification for the given "rules of thumb" and theorems.

par Farsan

Sep 29, 2016

Very good introductory course for inferential statistics. It is wise to complete the first course Introduction to Probability and Data of this specialization before enrolling into this one to grasp the concepts.

par robert p

Aug 28, 2018

This course seemed unbalanced compared to some of the other courses and was very work-heavy. I felt it could have, or maybe should have been broken up into two courses, or that other courses should be longer.

par Amit C

Feb 18, 2019

The course is very well explained I had to refer other materials for ANOVA technique to understand it better hence that part can be either improved OR more reference material be provided

par Zhang Q

Nov 23, 2018

Very useful course about statistics. May need some fundamental understanding of statistics before, but through the clear explanation and examples, I've learnt a lot from this course

par Paul N

Aug 17, 2016

The teaching is good, the course is a little heavy and a lot to take in in the later weeks. But, as a further grounding for statistics and R, I would very much recommend it.

par Adara

Oct 17, 2017

It is a very nice course, I have learned a lot. However, it is convenient to take the previous one of the specialization, as they base some examples or R knowledge on it.

par Abiodun B

Mar 28, 2017

This course gave me the toughest time of my life. I did the course for 3 months, i failed project once but i thank God, i proved toughest by passing with 100%.

par Sergio E T

Jan 04, 2019

The inference function and hypotheses tests are really useful. Permutation tests need more explaining and examples; otherwise they should not be included.

par Aaron M

Nov 28, 2019

A good course for learning statistical inference, though I found that more than a week per module was required to really absorb the content.

par dumessi

Aug 13, 2019

It is a great course, while some underlying logics are not clearly explained. And the quiz has some unexplained context, which is confused.

par Lucía M F

Apr 23, 2020

It was a very interesting and useful course. To improve it a little bit, I would focus more on the use of R to do different analysis.

par Janusz P

Apr 29, 2018

I liked this course because it gives basic ideas how inferential statistics works, without going into mathematical details.