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

4.7
stars
4,369 ratings
1,024 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.

HD

Mar 31, 2018

The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.

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751 - 775 of 1,000 Reviews for Introduction to Probability and Data with R

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.

By Shawn T R

Jul 26, 2017

Pretty good course and I definitely came away with a good understanding of the fundamentals of basic statistics. The video lectures move pretty fast and kind of assume that you're getting everything which is not always the case. The student forums help a bit, but responses either from the instructors or other students is unpredictable and inconsistent, so you really can't depend on it when you need clarification or can't understand a concept. I had to go outside of the course several times to really grasp all of the concepts. Fortunately there are plenty of resources on the net that can help though.

By Aparna P

Jun 04, 2020

The course is well designed and organized. I felt like I left having a clear understanding of basic statistical concepts and their applications to data, and what kind of questions one can ask of large datasets.

The only aspect I'd say that could be improved is to provide more comprehensive R skills that help students better prepare for the final assignment - At least for me, who has never used a programming language before, it was a huge challenge, and I had to look up a lot of additional resources to begin to grasp how to use R to approach the data.

By Mikkel R

Feb 13, 2019

Offers a good introduction to probability as promised. Great material, you can really tell that the teachers have made an effort making the content presentable.

The only thing I did miss however, was a lecture introducing coding in R especially since that is what makes up most of the time in doing the peer-reviewed assignment. Nothing fancy, just a single lecture introducing the logic behind the dplyr and ggplot2 packages would have been ever so helpful and could have been covered in less than 30 minutes.

Thanks for a good course!

By Mailei V

Apr 07, 2017

The stats info and exercises were helpful for learning stats/probability. The labs were somewhat helpful, but did NOT prepare you for the project at the end of the course. Prerequisites say not necessary to know R coding, but I REALLY struggled getting through the assignment in a timely manner and eventually wasn't happy with what I had to submit to make the deadline. I highly recommend taking a few courses in R in DataCamp or watching videos about R Studio (dplyr and tidyr) before attempting the labs and project.

By Jack M

Nov 02, 2017

This course is a solid introduction to conducting statistical analysis using the software R. The video and reading materials are clear and informative, and ideal for those with little to no familiarity with the theory behind probability and statistics. Outside of the weekly assignments, there is relatively less exposure and direct instruction for operating R, but I was able to make use of guides and forums in the coursera page to teach myself about R commands and codes as I navigated the provided datasets.

By Gregory G

May 05, 2020

Excellent lectures. Final project requires at least an advanced-beginner knowledge of R, especially of dplyr and ggplot2 packages. Not sure that's clear from the description. I came in having taken a short Intro to R course -- I'd have been overwhelmed by the data analysis project had I not. I relied on numerous YouTube R tutorials to get it done (thank goodness for the generosity of the R community!) and ended up really pushing myself. The instructor is a star. Many thanks if she's reading this!

By Joshua W

May 04, 2020

Overall, I recommend this course. It has the right balance of instructed work and explorative work (when it comes to the lab work). I would have wanted the probability calculations to have been more closely tied to the R programming in the lessons. That being said, I have no prior experience with R and I come from a humanist academic background, and I found the course material instructive enough to help me learn how to code with R and do basic probability mathematics.

By Shawn G

Oct 09, 2016

Great videos and testing structure. Great feedback to all of my questions in the forums too. 7 hours a week seems about right for me, with the recommended reading and watching the videos. However the course got continually harder and harder and by the time I was at the final exam I was really worried. In the end it got a bit difficult and stressful but I do feel it was valuable, and like I said, getting the feedback, even on weekends in the forums, helped a lot.

By Chris S

Jun 27, 2016

The content of this course is not terribly difficult, I thought, but it's a very good introduction into most aspects of Data Science. You learn to familiarize yourself with R quite well and get a lot of independence to create a final project based on a huge data set. One thing I would've liked is a sample completed project, start to finish, to see what was expected- the things that got produced (which you peer review) varied hugely in quality.

By Alfred Z

Sep 09, 2020

As a non-stats brain, overall really good introduction to stats.

The final project for this course seems pretty difficult and is a huge difficulty jump from previous weeks. It requires a good grasp of manipulating data with R and plotting. Regardless I found it very satisfying to complete. The final project is a bit frustrating, as the people peer-reviewing my project didn't put any effort into it. I was hoping to find some critical feedback.

By Tom B

Jan 17, 2018

Great introduction/review of basic stats concepts. I think the course designers assume a knowledge of/familiarity with R beyond what they claim in the Course Description. The weekly labs were somewhat helpful, but could benefit from providing the students with a bit more instruction on the functions of R. The learning curve during the Week 5 project was a little steep for a complete novice like me, but overall I found this course worthwhile.

By Alycia K

Mar 11, 2018

I had to drop this course because I had too many other things on my plate, but hope to enroll again another time. I thought the course was well structured, with very good examples, or explanations, of experiment design. Thanks to the instructors for providing free access to external materials. I thoroughly enjoyed what I was learning. Currently, the only reason I didn't give 5 stars is because of the trouble I kept having with R Studio.

By Alison W

Aug 25, 2016

The course videos are good, but the R programming is not well explained. I've enrolled in other Coursera courses that use R, Python or Octave, and they all provide clear demo videos for beginners to get up to standard with the code. This course doesn't do that so it's not a good intro to R, which is a shame because working in statistics these days is all about using R and similar tools so there should be a stronger emphasis on that.