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Learner Reviews & Feedback for Inferential Statistics by Duke University

4.8
stars
1,887 ratings
358 reviews

About the Course

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...

Top reviews

MN

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!

ZC

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!!!

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276 - 300 of 353 Reviews for Inferential Statistics

By Prasenjit P

Sep 14, 2018

Superb !!!

By Gustavo G

Apr 22, 2017

I love it!

By Donal G

Jan 07, 2017

Very good.

By chenhyde628

Nov 21, 2016

very good.

By sumedha

Jul 01, 2020

Thank you

By manuel e c e

Oct 27, 2017

Thank you

By EXAL G S

Feb 17, 2017

Excelente

By Alfredo J N

Feb 10, 2019

Amazing!

By Raj K P

Sep 18, 2017

good one

By gerardo r g

Jul 23, 2019

awesome

By Guangyuan L

Aug 02, 2018

amazing

By Guangjian D

Aug 10, 2016

5-star!

By Lerner

Sep 17, 2020

Great.

By Praveen S

Jun 04, 2020

Super

By Charles G

Jan 20, 2018

Great

By Gonzalo C S

Jul 24, 2016

Cool

By Sanan I

Jun 04, 2020

.

By Saravanan

Feb 01, 2019

-

By Radoslaw T

Mar 18, 2018

O

By Emanuele M

Aug 18, 2020

Overall a great course. Very rich in material. I do not have a strong math or statistical background and i struggled a bit with the range and quantity of material presented. Hard work is surely involved, but it is ultimately rewarding. A word of caution : if you are taking this course standalone (or as part of Coursera's Data Science Learning Path like me) without taking the first introductory part, you will have to compensate a bit on the programming parts if you are new to R (luckily a lot of freely available instructional material is found on the web, and the professor herself offers a free statistics textbook with online R labs). Not a downside for me, as this course has made me discover this fantastic language which has taken a strong position besides my budding Python skills. Cheers!

By 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.

By 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'.

By 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.

By 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?

By 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.