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Learner Reviews & Feedback for Introduction to Probability and Data with R by Duke University

4.7
stars
4,176 ratings
976 reviews

About the Course

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. 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 concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization....

Top reviews

AA

Jan 24, 2018

This course literally taught me a lot, the concepts were beautifully explained but the way it was delivered and overall exercises and the difficulty of problems made it more challenging and enjoying.

BB

Sep 04, 2019

Very clearly explained and the pace is awesome! I really enjoy each deadline and l can already see how it is impacting my day to day work and life. I ook forward to completing the course! Thank you.

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701 - 725 of 954 Reviews for Introduction to Probability and Data with R

By Mohamed A

Jan 12, 2017

Thanks

By Shreyansh S

Jun 22, 2020

Great

By Nimish B

Sep 19, 2019

Great

By Shiva P L

Jul 16, 2019

great

By Candice

Dec 16, 2018

Good!

By Christopher T O

Aug 08, 2016

Great

By umershahid

May 20, 2020

good

By 林昌璟

May 07, 2020

Good

By Khawaja M O

Dec 23, 2019

BEST

By MAYANK N

Nov 08, 2019

good

By Ishita J

Nov 08, 2019

good

By Finn张策

Dec 28, 2017

nice

By 林轩平

Nov 22, 2017

good

By Joseph S

Nov 28, 2016

Good

By 李纪玺

Oct 01, 2016

非常好!

By Divyesh S

May 25, 2020

na

By 葛佳妮

Aug 02, 2020

/

By Sanan I

Jun 04, 2020

.

By 张石磊

Nov 20, 2017

G

By Tobias M

Mar 21, 2019

This is a really great course, but it is NOT easy!

There is a steep learning curve for people who are beginners in R with the project at the end being quite challenging. The stats part of this course starts with a good learning curve and picks up speed quickly as well. The videos available for this course are generally good quality but could be ironed out a bit more in terms of some rough editing. You can see the age of the video material showing up in some of the videos.

Overall there is good help available on the discussion forums and using the statistics textbook and practicing with the example questions is highly recommended.

The time required as stated for each and every aspect of this course is vastly underestimated, a trained person will be able to finish this in the given time but a beginner can easily double or triple the time needed for the tasks.

Weekly time usage for class and all example questions plus digging for problem solutions in the forums is between 7 and 10 hours, the project at the end for an absolute beginner alone is easily another 10 hours hacking in R plus some extra time getting used to the project material.

Do NOT attempt this course if you can not dedicate enough time for this course!

By Jaclyn J

Sep 19, 2019

The lectures in this course were fantastic. The professor clearly explained complex topics, and I would gladly take more courses from her. However, I can't help but think that I wouldn't have been able to complete the final project successfully had I not taken previous classes in R. I don't think there was good alignment between the course itself and the final project. Also, the final project involved complex survey design data, but it was not clear if we were suppose to account for this (I assumed not because it seemed out of the scope of the course). I asked this question to the discussion board but never received a response. It was less satisfying to complete an exploratory data analysis knowing that all my numbers were inaccurate because of not taking weights into account. I would suggest a different dataset for the final project.

By Mark D

Dec 19, 2017

I have been looking for this type of course for years that combine a solid statistics background and learning a new piece of technology. Most statistics class are cram classes where the student needs to choke down a formula, regurgitate it for an exam, hope it is correct, quickly for get it and move onto the next. This class is the opposite. Concepts are easy to understand, logically thought out and in bit sized-pieces.

Professor Çetinkaya-Rundel is the best statistics instructor I have ever had. She clearly explains concepts, backs them up with applied examples and the textbook is extremely well written. I enjoyed learning R but sometimes the statistics lesson and the R were divorced from each other. The final exam was an excellent concept but it was a little daunting and way above my pay-grade.

By Emmanouil K

Apr 03, 2017

Overall, I would recommend this class. I found that the preparation towards the final project could use some improvement, especially plotting using the 'ggplot2' package. Why not work on this during Week 4 through an assignment that works on this? This would have made it easier to focus on the research questions of the project and less on the graph making mechanics. Also, I found that the project was a little bit too open-ended and could have used some more input from the instructors' side. The material during the four weeks of the course was really good and thorough but perhaps a little too difficult to follow for people who have absolutely no background in probability theory. Maybe one should audit the course first and then decide whether it would be a good idea to formally enroll.

By James P

May 03, 2020

I came to this course already armed with some stats skills with the hopes of brushing up on my stats whilst getting to learn to use R. The course is a nice, easy to follow introduction to data and probability. I had no R experience at the start of the course and the weekly assignments helped me build my R skills. However, the R skills taught were not sufficient for me to tackle the final data analysis project. I had to spend a lot of additional time teaching myself R. Before or shortly after starting this course I would suggest others take some time to self-learn (through youtube etc) about data frames, r markdown, dplyr, producing nice looking tables of summary statistics to be shown in r markdown (for the final project), and ggplot2 for nice looking data visuals.

By James B

Feb 27, 2020

This was a very comprehensive course. I am currently undertaking a PhD in Medical Studies, and this course gave me a better understanding of the basic theory underlying Probability. This was a useful shoehorn into learning and (importantly) understanding Bayesian statistics.

I would have given this course 5 stars, although the end project was disproportionately difficult compared to the rest of the content. The description lead me to believe that no background in R was necessary, however this did not appear to be the case. I highly suggest having a firm grasp on R and R programming language before looking to complete this course.