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

4.7
3,373 ratings
762 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 740 Reviews for Introduction to Probability and Data

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 Ernest R

May 14, 2016

The course is OK, but in my opinion the price 69€ is higher for the material you learn.

Lower price probably more people take the course paying

Also is a pitty that people we do not want to pay, we could not have a final assignement.

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 Leslie Y

Jul 29, 2017

video lectures were good but the final project at the end was too loosely structured, and depended on you to go learn many features of ggplot on your own

By Nathan P

Jul 30, 2016

I took this course hoping for a fundamental education in utilizing R for statistical analysis. Unfortunately, this course focuses heavily on statistical methods and very little on explaining the R processes used. Good for introductory stats students, not great for those interested in furthering their knowledge of R.

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 Sarah W

Oct 20, 2017

well thought out and delivered course, but I would have preferred that it dig in more into the topics. Not necessarily more topics, but deeper treatment of the topics that were covered

By Mark N

Jul 26, 2018

stats instruction is good but the R part is weak

By Rahul K G

Jun 22, 2017

Good for a beginner.

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 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 Krzysztof P

Apr 22, 2018

Nice, but missing a lot of features for those who selected Audit Track. Due to missing excercises, the course without quizes is of very limited value.

By Rianne d B

Jun 26, 2017

I felt like I didn't have enough skills in R.

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 Jerome T

May 20, 2019

The course is very basic, so it is good for an introduction. Quizzes are simple but the final project takes a lot of time. Why should we have to answer three research questions? Two would be sufficient.

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 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.

By Himanshu S

Aug 04, 2019

Could be improved by providing better tutorials to use R, provide guidelines to use Rstudio desktop as Rstudio Cloud is quite slow and crashes (it crashed 100s of times on loading brfssdata).

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 am

May 16, 2016

Nice course.

But the week 5 project is a little vague. It would be beter if we had a lab assignment instead.

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 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 Aaron D

Jul 17, 2017

Mediocre. Not much in the way of help for using R

By Sailaja M

Aug 28, 2017

I think that the teaching and tutorials in R should have been included instead of letting the student figure out all the R coding.