Retour à Introduction to Probability and Data with R

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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....

AA

24 févr. 2021

I always wanted to learn statistics from scratch, but I never had a good university teacher. Here I found a good teacher and also the opportunity to learn whenever I want ( and skipping parts I knew!)

AM

7 févr. 2021

After trying several courses to get me started with R programming, this one came to the rescue and had all the info I wanted. It also provides a great way to practice through labs and a final project!

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par Yash G

•22 févr. 2020

Good course for statistics but not for R

par katie v

•11 avr. 2019

I wish they went over how to use R for beginners in the beginning of the course. I feel like the final project was stressful and piecemealed together from google searches on the web. I think they should give us a list of all the codes in the beginning of the course that we will use throughout the entire course.

par Richard E

•30 avr. 2020

Lectures by Prof Çetinkaya-Rundel: Excellent content for the time alotted and well-organized. The URL to the "Distribution Calculator" was consistently erroneous in the slides (=: You can find it here: https://gallery.shinyapps.io/dist_calc/. The source code for that web calculator is part of a github R-language project and can be found here: https://github.com/ShinyEd/intro-stats/tree/master/dist_calc . One of these days, I am going to write up an issue on her use of "set.seed(12345)" and see if she takes it seriously! Her code is not bad for a statistician. (=:

Very simple Math. Depending on your background, this could be dissapointing or a relief. Too light for me.

A lot of R-language, RStudio, and R-markup. Expect to dive in. Note that RStudio is still evolving so you can expect a surprise or two along the way (E.g. crashes, hangs). The web version of RStudio has its own issues. I would stick to the desktop version since you have more control of your own desktop. Windows users can gleefully reboot when RStudio hangs - is it RStudio or Windoze?

"Let them eat cake" should not be missed: https://speakerdeck.com/minecr/let-them-eat-cake-first-0a3bbf75-f6f1-42d5-8d2f-ac2ff741611f .

Warning to physical science students: This is not in any way a criticism of the course but there are no examples from Astronomy, Chemistry, Geology, or Physics. Only health and social science stuff. I would have liked to see course content applied to something like data acquired from a telescope such as TESS or Hubble but that's me.

The forum for the last week is clogged with requests to have final projects reviewed, detracting from the intended purpose. I do not agree with having lower division students review each other's work: student maturity level to assess and give feedback is lacking. But, I knew what I was letting myself in for.

Another warning to physical science students: The data for the final project is CDC health survey responses for 2013. Roughly, 0.5 million rows and 330 columns. Lots of non-response values in the data. Not my cup of tea.

par Gabriel H B

•17 févr. 2018

Very impressive course, certainly among the top five I've ever taken online. Course design is basically flawless. The lectures are clear, concise and based on interesting examples. The course also comes with a textbook for which you can pay what you want, even a price of zero. The textbook is also very well written and contains plenty of examples to illustrate concepts that are introduced and lots of practice problems too. This is crucial for developing a good understanding of the material taught in a course like this.

I do have one warning about this course, however. The learning curve for the programming aspect of it is very steep. It says no previous knowledge of R is required, but I don't think I would've been able to finish my final project if I hadn't already taken about 15 other courses (mostly on DataCamp) that focused on R programming. At the very least, you should take the free Introduction to R course on DataCamp before you start any of the labs for this course. Ideally, you should also get a DataCamp membership and work through about 60% of the courses on the Data Scientist with R track before even starting this course.

I realize that sounds like a lot to ask, and that I am contradicting the course description that was written by the instructors, but this course makes use of a great deal of the knowledge that is taught on that track, especially the dplyr and ggplot2 packages, from the first module. Dplyr in particular is a wonderful R package that does things I've always dreamed of while struggling to do basic things in Excel, but it takes a lot of practice to get the hang of it you will get much more out of this course if you already have some experience with dplyr (and ggplot2) before starting it.

par Rui Z

•5 mai 2019

I've audited several similar courses and found this one to be the best.

First of all, Dr. Mine is just so great at explaining things. There is no doubt that she's one of the best in her area, but she's also born to teach and communicate. She combines all kinds of way to make a concept vivid and clear. I've audited couple other courses, and I took relevant courses back in college a while ago, Dr. Mine is the best out of all the professors I've met at explaining things. This is not in this course but next, but just as an example of how clear she is when explaining standard deviation of sample means. She takes time to combine a specific example, visualization, and simulation, to really make all the points clear. You could try to listen to her on that part in the next course week 1.

Second, the R practice in every week is very beneficial and helpful. The cases used in those practices are fun to work with too. The hands-on experience on R and data exploring is valuable.

Overall, this is a very helpful course for me to review probability that I took a while ago in college and almost forgot, and for me to learn R and get hands-on practice.

par Jenny Z

•14 août 2016

This course is definitly suitable for learners who don't have any related background. Dr. Mine Cetinkaya-Rundel has an amiable speaking style and always highlighted the key points in teaching videos, which helped me understand other contents in the textbook. Besides, the time and assignment arrangement of this course are also very reasonable. The only thing I could complain about is a system bug of the Amazon AWS and the grading system. My final project file went blank after the system told me the file uploading is sucessfully completed on August 3, and I got three 0 point from three peers since they only saw a blank file, of which I had no idea, and all I could see is "Grading in progress" on the system. Until the final grading day is over, which is August 12, the system finally reminded me of this horrible thing. I came to mentors, disscussion forum as well as the help center, reuploaded my file, desperately tried to find peers who can still spare some time to review my file, and it is finally fixed today. I got my certificate in the end, but the grading process is really frustrating. Hope this bug won't happen to anyone ever ag

par Hao C

•6 nov. 2019

Teaching: I really like the clear and concise teaching style of lecturer and the wide range of simple real-life example used to explain the course content. I’m a social science student, given I’ve studied quantitative research methods before, this course is easy intro to and good refresher of data and probability theory. This course really gives me some confidence to continue to study probability theory, after finishing this specialization.

Textbook: The textbook used in this course is a good supplementary material, although it is not necessary to read the textbook. Course videos have already explained everything that we need to know at intro level. The textbook also covers some extra optional topics that are worth reading.

Course Structure: The course structure is well organized with clear focus in each week.

Assessment: The assessment of quiz in each week is relatively easy. The exploratory data analysis required in peer-reviewed assignment is relatively difficult for beginners. However, the course mentor has drafted an easy-to-follow guide in the discussion section which is really helpful for finishing this assignment.

par Roel M

•30 oct. 2020

I liked the course very much, as it really did a good job to refresh my previously learned concepts in basic statistics and probability. The use of RMarkdown was not that easy, but somehow manageable with trial and error, and the knitting to html was also a little cumbersom.

My main problem was that the week 5 assignment was not very clear and demanded more from the inspiration and creativity of the student in coming up with original research questions than on how to handle them and put them in R. As a consequence, the line of thinking risked to be reversed: first you realize what you can handle in 'R' and then, you create a question that can be handled... Perhaps, I would have preferred a first research question, formulated by the teachers, where only the concepts and programming already taught are to be used, before letting us free in the wild. Another consequence of the total open question is that the more fancy assignments that I could review, were those that used more R packages/commands than we were supposed to know already... But still, overall, it was a good course, sometimes challenging, which is good!

par Anna D

•3 avr. 2017

Best statistics course I've ever taken. So many Aha! moments I can't count them.

I have struggled for years to understand and get the hang of statistics, at uni, with online courses and at work. With this course (and the following courses) I think I have finally gained a DEEPER understanding of some of the basic but very important concepts of statistics. Lots of detailed examples and no overly complicated maths gibberish (although still mathematically sound!).

The R programming bits run in parallel to the statistics lectures and can be followed (necessary for a certificate) or can be ignored (if you only want to grasp the concepts), but are overall very easy to understand and follow. There is only little background to R as a programming language and the different types of data, lists, matrices etc. To me that's a good thing, as it allows you to use R right away (which in turn makes me more motivated to go back and learn more about R).

I whole-heartedly recommend this to anyone who wants to understand and use statistics!

par Raw N

•23 avr. 2017

Very well put-together course.

I like that the course has in-video quizzes as well as practice exercises to help prepare you for the weekly quizzes. The labs for the course are also very helpful.

The textbook that accompanies the course is freely available in pdf format online and the suggested exercises are a great complement to the rest of the course materials.

For those unfamiliar with R, the project is a bit of a leap from the rest of the contents in the course. To get around that, I'd suggest to both use the discussion forum (posts by mentor David Hood are particularly helpful) and to take both the R programming course and the Exploratory Data Analysis course from the Johns Hopkins data science sequence. Those 2 should together be doable in 5-6 weeks and at that point you should have sufficient background to where doing the project in this course (and those in follow-up courses in this specialization) should not be a problem.

par Tural K

•25 nov. 2020

Before starting the course, I was not expecting it to be ghat much effective. The statistics classes were informative. However, the main reason that I took this class was because of R studio and implementation of statistics via R studio. First 2 weeks were perfect, the only lacking part was the 3rd week (I think R assignment could be better. Obviously, we did not need much effort to complete Week 3 R assignment which I did not like). Week 5 project, on the other hand, was just on point. We could apply everything we learnt throughout the course; especially, reviewers were strict and gave constructive feedback which I liked a liked a lot. One more thing, some people might want to start learning R studio via video tutorials, which this course does not have. However, R markdown was very good method for learning purposes. Overall, very solid one. Thanks!

par Vladimir V

•10 févr. 2018

I think this is a very good entry level course for those who are interested in entering the realm of statistics.

The learning objectives of each week are well defined and the practice and weekly tests are based on those learning objectives. The videos explain very well each objective in a very convenient, easy to comprehend and interesting manner. For students who want more 'after class' material, the course offers a very nice book, which I personally used and helped me a lot during the course. The book also offers practice tasks at the end of each chapter.

The course project: Personally I think the course project in the 5th week of the course is interesting in a way that you have actual data to work with and use almost everything you have learned during the course.

Overall I think this is a very good course!

par Natalia S

•15 juin 2016

This course was excellent, the teaching material top-notch and with excellent pedagogy. It's amazing that the course authors offer a statistics textbook almost exactly covering the course content for free. The idea to combine R and statistics is right on the money too, thanks to this one can learn 2 skills at the same time, with statistical analysis letting you practice coding in R and R helping you visualise your statistics. The lectures are divided into small, easy to absorb chunks and the teacher does an excellent job explaining the material, giving very good examples and analogies to help the students understand concepts. The exercises and assignments are fun to complete, and the course offers a flexibility in how much time you spend on it per week, e.g. there are non-mandatory exercises to do.

par Tamir L

•25 juil. 2016

This is a brilliant course that makes statistics and probability as approachable, engaging and clear as humanely possible.

Prof. Mine Cetinkaya-Rundel explains every subject very clearly, and has included some very effective quizzes and lab exercises.

I first encountered R markdown files in this course and have used them constantly ever since.

My only tiny point of criticism is that the non-graded exercise quizzes are way easier than the real quizzes, and do not really prepare you at all to the more complex questions in the actual quizzes. It's a petty and unimportant kind of criticism in an otherwise wonderful course.

If everyone taught stats like Prof. Cetinkaya-Rundel, this important subject would have been a whole lot better understood and utilized globally.

par MARIO J G M

•14 mars 2018

Excelente. Es un buen curso introductorio. Hace particular énfasis en las distribuciones normal y binomial. Da una pasada introductoria a R que, entre otras cosas, no es enseñado durante las clases sino que a través de los talleres que se realizan al final de cada capítulo. Son explicados con solvencia conceptos como correlación, causalidad y generalización.

Para quienes no saben, desconocen o no han tenido contacto con markdown valdría la pena ver un par de vídeos en youtube. Yo manejaba algo de R, pero nunca había tenido contacto con markdown y me pareció una herramienta muy útil, y aunque no es explicada en las lecciones o en los talleres, el proyecto de final del curso debe ser hecho en markdown.

par Matthew L

•9 août 2016

Professor Cetinkaya-Rundel's explanations are clear and she gives many examples, the quizzes are fair and I think it is an excellent idea to have a lab in R to get students familiar with that tool.

I recommend that students read the book chapters and do the practice problems there, it's very helpful.

My one criticism is that the amount of R taught in the course is not really enough to do a good job on the capstone project, because the data in the given database is formatted very differently. I think maybe the course staff could reformat the database to make it more user-friendly for beginning R users, but in the meantime you may want to study a little R on the side at, say, DataCamp.

par Tascha S

•29 avr. 2020

Very, very useful course. Exactly what I was looking for. You have to do some research beyond what's covered in the labs to really get acquainted with R, but there is so much available online that it wasn't an issue for me. The open-intro textbook is fantastic, and the course lectures help summarize the textbook info in a rich way that adds to the textbook content. The suggested problems, quizzes, labs and final project were all fantastic for reinforcing the content learned and actually putting it in practice (as most of us learn best by doing). All in all, I'm extremely pleased, and I'm moving onto the next course in the Statistics with R progression with much excitement!

par Aaradhya G

•22 nov. 2019

Absolutely amazing! It is clear that the professor, Ms. Mine Çetinkaya-Rundel is passionate about the subject and knows it inside out. The practical example-based approach to learning is appreciated, since a lot of statistics courses don't give learners a realistic setting to think about their knowledge, leaving them with the infamous 'how will this help me in real life?' question. The book, OpenIntro is also very helpful in this regard.

The R course has been introduced nicely too. The difficulty curve might take time to get used to, but the packages introduced and the codes used make sense, so it should not take too much time.

Wholeheartedly recommended!

par Mariusz S

•16 août 2016

I really liked this course.

The course comprises of lectures, which are clear and are rich in examples, and of practical assignments, which you do in R.

The practical tasks is where the course shines - everything is explained very clearly, there is a lot of content, and the course works with databases that are huge (thousands of cases and hundreds of variables) and have some of the more common problems (eg. missing data). I have little to no prior programming experience, just for the record.

Mind you, this is an introductory course, as the name states, so don't expect to be a master of R or data handling after finishing it, but I feel I learned a lot here.

par FLAVIA N L A

•6 janv. 2020

Excellent course. Classes were intense and the professor was very didactic. It took me around 10 hours a week of dedication, and the Final Project of the last week required around 40 hours of work. I am very pleased with the final result, but I think it is important to let it clear the real time expected of effort here. Unless you are already really familiarized with the concepts and with the R platform, the course requires a strong commitment. My final verdict: I am very grateful to have done a course of this quality from where I am. Thank you: professors, mentors, developers and fellow classmates! Every minute of my time was worthwhile with you.

par Neringa B

•5 oct. 2017

Introduction to Probability and Data (by Duke University) is an excellent course. It's like a beautifully and clearly presented piece of the history of statistics. This course must be taken by all who are interested in the type and dynamics of relationships between various elements of life. At the same time, the duty and responsibility of mental reality (=ideology) is to reflect the actual reality. The dogmas of the traditional statistics revolve around a traditional family model which excludes present day gender diversity. This makes traditional statistics no longer reflective of the actual reality unless it incorporates gender diversity.

par Blaize G

•3 sept. 2019

Very rigorous course. No you don't need programming experience to complete, however you will be thanking your self if you spend as much time throughout the week learning R as you do on the content of the course. I was stuck at the week 5 project for a while until I buckled down and spent a few days on Youtube and on Google learning R. So with that said, if you have no prior exposure to programming or Stats, this course is very difficult, however if you stick with it, it is very worth the time spent. My tip is to reset your deadlines as many times as you need to if the content is more difficult than you anticipated.

par Tanika M

•18 mai 2020

I really enjoyed taking this course. The accompanying book builds builds and teaches the concepts very well, and the videos are great resources to help solidify the readings. There is very occasionally some tricky wording in a quiz question, but it is barely an issue. Like many other reviewers, the Week 5 project stopped me in my tracks at it felt very daunting, but I then found further information from instructors/mentors on the forums, which were a great help (I would suggest maybe sharing some of the forum information in the project description, or linking to the forum more clearly for further support).

par Yi-Chien, C

•23 févr. 2017

The course was well-structured and the instructor clearly illustrated statistical concepts to students who have no prior experience in the field. Although the lab assignment for each week may seem a bit stressful for beginners, the overall learning is highly inspiring and does prove rewarding as students finally get to apply the technical skills to their final project. Also, the guidance for each lab assignment was very helpful. It would be even better if there are example code answers for the lab questions, since some of the questions are a bit more complicated. Overall, this is a worth-taking course.

par Marwa A E K

•18 juin 2019

Though you may feel at the beginning that the pace is somewhat fast, but you'll learn a lot if you stick to the material and worked on the labs and the hands-on tutorials. Not to mention the project example in week 4, it was incredible I really liked learning through the errors and interpreting what are these errors and why they may arise. In the project I learned a lot, I felt it's not an easy task to start working on a dataset from A-Z with complete freedom to formulate research questions, clear the data and get the appropriate inference! Overall it was a great course that I really enjoyed :)

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