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

4,389 ratings
1,030 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


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.


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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926 - 950 of 1,007 Reviews for Introduction to Probability and Data with R

By Indrani S

May 25, 2020

very helpful

By 김인수

Jun 25, 2019

good lecture

By Md M H

Nov 13, 2018


By Sanjeev A

Jul 02, 2018

Very Good

By jaime p

Mar 15, 2019


By Zhai H

Oct 10, 2017


By 徐天宇

Nov 15, 2018


By FangXinyi

Jul 23, 2018


By Subhadra M

May 30, 2017


By Marcin W

Apr 29, 2017


By Philippe R

Sep 05, 2016

Very mixed feelings about this course.

Generally speaking, the course lectures are informative and well organized. Mentors are reallly of great help, they are doing a great job, honestly: they are very active, they give good insights, they know the subject matter.

But in the course lectures, there are occasions where concepts are used which were not formally introduced before their actual use.

One example: in the lectures on probability, the first "slide" in the lecture talks about random processes, outcomes of random process,... On the next slide, the notion of probability of an event is introduced, but the very notion of "event" was never introduced. It is introduced in the accompanying book, but if it is the case that the book chapters should be read PRIOR to watching the course videos, that fact should be made clear.

Further in the course on probability, some words are used "interchangeably" without the context making it clear why they can be used interchangeably. For instance, on some occasions, the concept of independent events is used, but then, later on, the discussion talks of independent processes. Which is which??? Is there a difference? If so, what is it? When do I need to use independent events as opposed to independent processes?

The graded assignments are of varying quality. The most disturbing thing about them is that, on some occasions, concepts are used in the quiz questions (either directly in the questions and answer choices, or indirectly in the "correction" for the quiz after you have submitted it) that were never touched upon in the course.

I have had two occasions of concepts not introduced in the course but used in the graded assignments.

The first occurrence of a gap between course content and quiz questions was on a quiz question about inference. I failed the question, and understood why I failed based on the course content litterally minutes after failing the question (and one mentor actually rightly corrected me). But the question "correction" (the explanation text you receive after submitting, as justification for what the correct answer is) referred to the concept of "two-sided hypothesis test". Where did THAT come from?? I checked and rechecked the course videos, no mention at all of it. I checked the accompanying book, and the first mention of two-sided hypothesis test is way way way further in the book, in a chapter that is entirely focusing on inference.

The second occurrence was in week 4. The course lectures cover two distributions: normal and binomial. The recommended reading in the book also focus on these two distributions (the recommended reading actually skips the section on geometric distribution, if I remember well). But in one of the quiz question, there was one of the possible answers referring to the geometric distribution. If it is the case that we are supposed to know and understand about geometric distributions, then the course content should cover the subject. Or at the very least, the course lecture should mention clearly that learners are advised to read about it in the accompanying book.

The guidelines for the project assignment (week 5) are not all that clear as to what is expected from the learners. Sure, there are instructions on where to find the info, what structure should be followed,... There is also a very nice "example" project (designed by one of the mentors), which provides a lot of useful info (how to filter missing values from variables,...).

But there is no real hint as to the depth of analysis we are expected to complete. This is definitely a source of confusion, not only for me, but also for a few other learners, from what I gathered in the discussion forums. The result is that the projects you get to review are of very disparate levels. Some end up in calculating one figure per research question, without any attempt at deriving trends or patterns, others do not include any plots at all,... The thing is that the peer review criteria do not really provide a good basis to ensure that learners did indeed assimilate the course contents. Most of the questions in the peer review assignment have a lot more to do with following a canvas and not so much with the course substance itself.

For instance, some of the peer review criteria have to do with the narratives for computed statistics and plots. The criteria are: "Is each plot/R outout followed by a narrative", "Does the narrative correctly interpret the plots, or statistics", "Does the narrative address the research question". But when the research question is a question of the type "What it the IQR for income per state", for instance, the narrative can be very short: "IQR per state shows that the state with higher variability of income is...". So, the narrative meets the 3 evaluation criteria: there is a narrative, it does address the research question, and it does correctly interpret the statistics. But it is not particularly useful.

I do understand that Internet-based peer review is challenging, and that you have to settle for "neutral" criteria that are easy to assess by learners. But the peer review grading "grid" as it currently stands is not "that" helpful in assessing whether the course contents has been assimilated.

To conclude, when I took the course, my initial plan was to follow the entire specialization. But after having completed the first course of the specialization, I have radically changed my mind, and will look for alternatives "elsewhere" to get the knowledge/skillset that I am after.

By Casey S

Nov 12, 2017

This course to me had some very clear un-explicit limitations, pros and cons:

- The lectures are fantastic and have a good sequence for beginners

- The course is very holistic in its approach, meaning that it covers theory and application very broadly and gives you a good sense of how different aspects of the field of statistics relate to eachother

- The coverage of the R programming language is insufficient for the requirements for using it in the final assignment, I can't stress this enough for beginners. I highly suggest you take a foundational course in R, highlighting syntactical structure of the language, prior to taking this course

- The labs are great for learning the primary components of R, but they don't give you real practice coding. There is very little to no explanation of certain functions in R and there are no videos on it. I do not feel at the end of this course I have a very good understanding of the structure of the language of R, I do however feel I was assessed as if I should have.

- I felt the quizzes were appropriately rigorous for a beginner such as myself.

Most important bottom line is: If you are a true beginner like myself I urge you to first take a course more targeted to R before starting this specialization. Otherwise, like myself, I think you will feel very overwhelmed at the end.

By Jeremy L

Jul 06, 2018

The course is divided into 5 sections, each of which you have a week to complete (if you want a certificate). The first 4 sections/weeks are well designed and involved a mixture of lectures (most were good), reading assignments in a textbook (free online access), practice problems, and a weekly quiz. Along the way students learn how to use R through a handful of walk-through examples. In general this works. That said, the last two R assignments are a mess. For the 4th week, the instructors put together a demonstration for using R to ask and answer some basic research questions. The document they put together for this demonstration, however, is so full of typos and grammar mistakes, and worse, heaps of nearly incomprehensible sentences and phrasings, that it is almost worthless. It was really painful to get through it. The final R task is to work with a real-world data set, ask a few research questions, and use R to do some basic statistical analysis of the data. Working with a real-world data set is great. That said, I felt as if the instructors were asking students to do far more with R and statistics than we had learned in the class. I saw many similar opinions about this assignment online. And in grading my peers, I noticed that other students didn't know how to complete the project either.

By Nayyer I

Apr 28, 2020

The course is great in terms of building foundational concept for data analysis and lab assignments were ok. The I think often the time listed for any class is a little underestimation of time commitment. The course has offered me a lot of new concepts to learn and was good refresher for many other. But the final project was a huge disappointment for me. The students have been given a huge datasets that leaves students struggling to figure out where to start. In order to understand the data, students have to go to various links to see what the data is, how it is collected and definition of each variables. Then there are more than 300 variables and you need to pick few to do something you think is interested. Finally, the project needs a good level of expertise in "R" and course does not teach you that at all. I would suggest that for future courses reduce the number of variables depending on what most students have been using. Draft a quick summary and report about key information of data and share that on course page rather than links to web pages, and finally let students use the software that they may feel interesting. Not everyone is skilled to use R.

By Efe A

Feb 24, 2018

The videos, readings and quizzes are excellent, they are well organized and follow a logical sequence. The level is also suitable for a beginner and pleasant enough to watch after a busy day at work !

However Rstudio/r instructions and lab assignments need improvement. The specialization description puts a lot of emphasis on R giving the impression that these skills are also going to be taught from scratch. However there is not enough instruction and feedback. Judging from some assignments I have read it definitely seems like most of the students already have a comfortable working knowledge of R. If you are like me, a complete beginner, you will have to learn a lot from additional sources and your assignment will look like a mess (but you will most likely pass !)

By Ted T

May 05, 2020

I didn't get on with this course, I'm afraid. I found that the R explanations were somewhat lacking for what I needed. Some sections I could complete absolutely fine but then it got suddenly much harder to do what was required. While that's part of learning and language, it wasn't helpful for the stage in my learning experience (during each week's classwork).

I'm not an idiot. I was able to complete the full course, including the last stage project but it just didn't teach all that well. I don't feel much more confident in R.

I'd also say that the course relies quite a lot on the open source textbook. The instructor did write it - fair play - but I'm paying Coursera for the privilege of following something that's free.

By Michael S

Oct 07, 2019

The course lectures were very good and informative. However, this course does need some work. First, the text revision references were confusing. The homework assignments were a confusing as to where they should be performed; on our own or within GitHub. I have used R before and was using this course as a refresher. The course series definitely needs a optional introductory course in use of R, R Studio, GitHub, and R Markdown language. Similar to the JHU Data Science specialization. Finally, the course project was a bit deep for introductory Probability and Data. Need to make the course project less demanding or drop the need for a final course project until later courses

By Nicholas R

Oct 05, 2017

Problems with the course: Despite getting high grades, I felt like I had forgot much of the material by the end. There should be more quizzes, a mid-term, and more peer-reviewed projects. Didn't teach R basics which made it hard to learn the language and complete the final project w/o a lot of research. Content was generally great.

Problems with the platform: Video skips randomly, submitting ID was buggy (wouldn't save), and the chance that you might not get enough peer reviews and have to delay to the next session is nuts. Just require that people submit more reviews!

By Kaylee L

Mar 29, 2019

Since the reason I took this course was learning R programming, I think this course focuses too much on data theories. From my perspective, it would be better if this course could put more efforts on R programming skills. In addition, when students raise questions on the forum, these questions were seldomly answered by tutors. It is obvious that there were some bugs of coursera platform for a long time, but these bugs were not fixed. However, I learnt how to start R programming by joining this course, which was really helpful to me.

By Alexander S

Oct 15, 2019

On the whole I thought the theoretical content of the course was good, and that the supporting materials were quite helpful. I would strongly caution prospective students about the amount of time that is actually required to complete the course requirements. Specifically, I found that the amount of time that was, in actual practice, required to learn even the basics of R and to then apply this to actually doing the final assignment vastly exceeded the time suggested by the course instructions.

By Daniel H

Jun 04, 2019

The textbook is excellent, though it would be helpful to provide some suggestions for a more rigorous treatment of the material. Lectures are well presented and organized. Assessments (which, unfortunately, are what drive teaching/learning outcomes) are of a lower quality. The course project has potential, but poorly executed as a peer review assignment. I have no confidence that anyone with this credential will have met the course objectives. Don't hire based on this course.

By Yevgeniy G

Nov 20, 2016

Slow down. Introduce more R before asking to create projects in R. Only because I know other programming language was I able to finish week 5. Also very strong group of mentors... God bless you mentors!

Disconnect between course objectives and programming assignments / labs. Reading book you learn one thing, watching lectures another and then unrelated labs, which then culminate in something totally different during week 5?

By Omer N

Aug 28, 2017

The lectures are relatively good, though not of consistent quality. Some material is explained very welland some in a bit of a disorganized fashion. The assignments require a level of R knowledge which is neither taught directly nor stated as a prerequisite. For those familiar with cleaning and exploring data with R (ggplot2 and dplyr especially are important packages) this is an excellent course.

By Nikoleta K

May 13, 2018

It is a fine point to start for a beginner and you do learn the statistics part of the course in a constructive way, but I believe when it comes to learning R it is lacking. You get to learn coding, but not enough as in to be able to apply it in different sort of research! The teaching provided for R is limited and situational, and this is not because it is the introductory course.

By Noah W

Jun 19, 2019

Overall I learned a lot in this course, although that comes with a caveat. Some of the more difficult content was breezed over, and I found myself searching outside the coursework to get a better explanation (particularly with probability and most of the R tools.) That being said, if this course is useful as a series of benchmarks to guide you with your own research.