Retour à Introduction to Probability and Data with R

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1,205 avis

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....

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!

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!)

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par Alan S

•20 déc. 2018

This is not a course in how to learn R. It is a basic statistics course. The statistical content, including the lectures and book, are very good. The structure of the first four weeks of the course is very good. I recommend students follow the syllabus (videos, etc.) in the order shown--one week I tried the homework without viewing the videos first and it made it more difficult than if I had seen them.

I was very disappointed with the final project, because it required much more R expertise than was explained in the rest of the course. The "example project" was useful, but not sufficient. The specific requirements for the final project (e.g., two of the three subjects must involve and analyze three variables vice two), were not clear or easy to find. An additional week or lectures in the final week covering R tasks such as how to make an HTML document and project expectations would be helpful.

par Anastasia M

•4 févr. 2017

Lecture videos were fantastic! Instructor was amazing. I have a big problem with the final project in Week 5. The entire courses was focused on statistics, yet the final project was focused on R. I wish I had prior R knowledge before starting this course, yet on the front page it says it's a beginner specialization. I would recommend learning R and ggplot before this course. It will make the course a lot less frustrating!

par Susan M

•20 févr. 2019

It's so so bad. Idk if this is old or what.

Unfortunately my answer here got deleted I took forever writing. So I will just be frank.

God it sucked so bad

Even my bf who is an economist who uses R regularly had to take a half hour of googling just to set up R for the quiz to be usable. Before he came over I was just having a horrible time full of crying and remembering the trauma of computers eating my homework as a kid. Worst class experience of my life. And you call it a beginner course? With that little guidance? Seriously there is so so little about setting up R. I still feel so angry my time and energy was wasted like that. And I was so excited for this course- I'd been gearing up for weeks and was very very committed to finishing.

Steer clear of this course. Btw my friends tell my that R is so old and rarely used anyway so it is dumb to prioritize it over Python or even modeling in Excel

Zero stars

par Korawat T

•25 déc. 2016

This course will teach you a little bit of statistics and leave you confused with R. The first four weeks are very straightforward. They give R code for everything. However, this is not good because it does not prepare you well for the last project in which you have to do the whole project using R. This is almost like learning how to cook without knowing how to use a knife.

par Akshay J

•5 janv. 2019

I have a major in mathematics and this is by far one of the best courses I have ever taken on introductory statistics. Instructor explains all the concepts clearly with tons of examples. The labs are very well formed you will never be lost with them. The final project turns out to be fun and informative! Overall, it was a great experience. I recommend it to anyone wanting to get into data science field and/or improve their basic knowledge of statistics and R programming.

par Hyeon-Jeong S

•22 juil. 2018

I regret I have not actively participated in the course. The failure is mainly due to my ignorance, however, the course did not really teach "R". The theory of statistics is great but it is not properly linked with "R". I was misled by the title and teaser film. Sorry.

par Alexander S

•22 juin 2018

I took this course primarily for the purposes of learning R and reviewing statistics. While the course content was well organised and succinctly presented in the videos, questions on quizzes and labs could at times be phrased in a confusing manner (even for a native English speaker), and the labs did not prepare one for the finesse with R required for the peer-graded final project. For the final project, I would recommend establishing more specific conditions (e.g., the number of visualisations expected, suggestions as to variables to explore in the dataset, and the points to consider in narrative sections), especially for an entry-level course. As a university professor myself, I have found that it is more pedagogically effective to offer precise guidelines for lower-level courses, and reserve open-ended projects for higher-level courses/seminars.

par Syed S R

•13 sept. 2018

Not suitable for beginners

par Bernardo E

•7 janv. 2020

Everything goes smoothly but the last assignment: it's crazy difficult. Every week is a guided exercise but the last one has very few related to that. 2 stars out of 5 because nonetheless the probability part is interesting and well explained.

I didn't finish the course because it was too hard for me to fill the assignment. I paid 44€/month and the second month i decided to give up in order to save money - it would require me at least another month (they say 2h in total..).

Don't do it unless you have a prior solid knowledge of R.

par Jodie W

•19 mai 2017

I'm so frustrated by this course. I feel like I've learned nothing about R. I worked for hours but felt like I was just dropped in the middle and expected to already have an understanding of the language. This was NOT helpful!

par Sonal S

•27 juil. 2017

Great course! Explained the concepts so clear and crisp and the exercises with R are great. The project reinforces all the concepts. All in all, a great course for beginners in statistics and R.

par Rafael A S R

•18 sept. 2016

Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher

par Raluca B

•6 mars 2020

It is an excellent introduction to probability and data. Concepts are explained really well, one of the best courses (not only among MOOCs).

I think it would be improved by adding one week to the course dedicated to solely data analysis in R, as a precursor to the final project. There was too big of a gap in terms of R practice (for those new to R) between what was explained during the course and the final project. I would have found it super useful to have one more week in which to discuss how to treat missing data, how to clean data in R (even if just a simple cleaning, like getting rid of the NAs), followed by steps/do's and don'ts when analyzing data, different types of graphs in R appropriate for numerical and ordinal data, that sort of thing. That would have made it almost perfect.

par Cheryl L X

•15 févr. 2019

Be sure you want to learn R before you embark on this course. As a beginner, it was a challenge, but after a few rounds of revisiting the content, it all started to make sense. I would recommend you do the exercises on R Studio. I did mine on Datacamp and had to refamiliarise myself with the RStudio platform for the final assignment, which was slightly painful as more things had to be set up (and time may not be on your side by then). You can use the commands learnt in the course for the final assessment but many classmates seemed to go above and beyond. Online resources are truly indispensable and I'm amazed that I can make decent educated guesses as to what certain lines of code do, in order to improve the chart!

par Azhan A

•23 janv. 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.

par Breno B S

•16 sept. 2018

I would give it 5 stars if it was truly for zero beginners. I myself didn't have much problem understanding the content, but I can imagine that people with no background in statistics would have a very hard time. Some very important concepts are just glossed over. Another problem is that much more attention is given to the mathematics behind the stats, as opposed to how to conduct the tests themselves. I'm finishing the 2nd course two (inferential statistics) and I have the same feeling there. We spend video after video learning the nuts and bolts of the math behind, but at times we are only given the code in R. Sometimes the code is not even given!! The same with ggplots. The real-life applicability of the knowledge here is to make sure you are able to use the software to run the analysis. It's important to understand the logic behind, surely, but no one will, professionally, do the calculations by hand. I left this and will leave the second course (Inferential statistics) with the feeling that I've learnt much more the maths than how to actually use R.

One final thought: at times, in stats, the most difficult thing is to decide which test to implement. There are possibilities, but which one? Why? How to I check for Skeweness in R (the number, not the histogram). What is considered too much skewness? What is too large a bias in bootstrapping? These are just examples of precious, directly applicable information that's left out

par 舒穎 鄭

•25 nov. 2018

The lecturer is very nice and teaching well, the basic knowledge is easy to learn. Examples are vivid and easy to understand as well. The biggest problem is the final project. The things we learn can not support the ability to finish the project. One way to improve it is to give more tips or teach more usage of R. Overall I learnt many useful stuffs and I recommend it!

par Sandy W Z

•24 sept. 2016

There were key definitions and concepts that were stated wrong in this course. Please read the student forum for details.

par Guolong M

•9 janv. 2021

The materials of this course are very well organized. All the concepts are clearly explained with useful tips and caveats. The examples selected in this course are impressively practical, all of which are from real world data and are interesting to the general audience. It is worth mentioning that the free textbook used in this course, of which Dr. Mine Çetinkaya-Rundel is the author, has tons of exercises that are accessible, informative and enjoyable (and has answers to the odd-numbered questions!).

Regarding the homework, the difficulty of quizzes is well-set (not like some quizzes in other courses that are too easy or confusing). The RStudio assignments are not bad, though I think it should have been much better with video guides (or at least video reference); a wall of code and text is not friendly to beginners.

A side note is that math is not heavily used in this course; it is not a theoretical statistic course for disciplines like computer science or physics. if you are seeking for solid mathematical deduction this is not the course for you.

par Christopher S

•2 janv. 2020

This course had a good balance of easy and challenging content. I like how the reattempt feature of the quizzes doesn't just give you all the same questions again in a different order. But at the same time it doesn't completely change the set of questions. This forces you to go back a really understand the content if you want to maximise your mark. The final project seemed harder than I was expecting, but that resulted in gaining a lot more practice with RStudio, which really helped to learn it well.

par Lien C

•19 déc. 2018

Dr. Mine Çetinkaya-Rundel is an amazing teacher! I have never learnt and enjoyed so much statistics!

Syllabus is well constructed and organised, plenty of learning materials (with moderate difficulty). This first course is aimed at beginners (no requirement of prior statistics knowledge). Although I use statistics regularly, I don't really understand them well so I find this course extremely helpful!

Thank you so much for putting together this course of the whole specialisation!

par Guy T

•14 mars 2019

I've not studied at this level for a while so the first couple of weeks were intense. The pace didn't let up but the quality of the presentation material was excellent. I didn't feel quite prepared enough for the project and it took much longer than the estimate to complete but well worth it as an exercise.

par Betty B

•4 sept. 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.

par Harsh D

•31 mars 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.

par David K

•7 mars 2019

I liked:

+ The detailed Learning Objectives.

+ Good examples in the lectures.

+ The quizzes are great for testing and refreshing one's memory.

+ Overall the course seems very well-focused on the most important foundational items and hammers them in.

I'd appreciate improvement in:

+ Providing more clarity on how much R is expected to be learned for the final project, or lowering the level of R skill expected for the final project.

+ Providing a more relaxed time estimate on the final project. I spent 10+ hours on it, in addition to ~10 hours learning more R on DataCamp.

+ Getting feedback on my work from a professional, not just from fellow students. (I would be willing to pay for that.)

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