2 mai 2020
This course provides an introduction of some important concepts and tools on a very important aspect of data science: cleaning and organizing data before any analysis. A must for any data scientist.
25 oct. 2016
This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.
par Lawrence G D•
29 nov. 2020
Very challenging but rewarding. The first two weeks of material were a bit condensed I think, hard to follow how to import some obscure data types into R and too complex to be covered in a 5 minute video. Could have been spread out more or omit some that are not probably practically useful. The quizzes and the final project were difficult to navigate using only the material provided in the lectures, and had to rely a lot on Googling stuff.
par Kai P•
8 août 2018
The quality of this course is much better than the earlier two. Although this course still has the problem of feeling like a disjointed series of topics on singular functions, there is much more of a cohesive overall theme and structure so it feels a bit more like you're building towards an overarching goal. The final project directly relates to the lectures and felt like a solid way to connect most of the ideas to a project on real data.
par Daniel H•
18 mai 2020
I suggest changing the quizzes and assignment questions more often because they're all over the internet for this course and rest of the courses in the specialization. I understand that students who are cheating are mostly hurting themselves, but it also affects the value and credibility of the certificates you're giving out.
In terms of course modeling and content, it's very nice. I really enjoyed. The swirl package is genius. Thanks.
par Mary S•
20 avr. 2016
There were a lot of good nuggets in here, but overall this course felt somewhat disjointed compared to the others. It would be nice to have more practice with some of the different formats (e.g., JSON) and for exercises to loop back to some of the early content. I did like that the final exercise required a fair amount of investigation into understanding the documentation and relationship between the files before undertaking to code.
par Mark B•
7 avr. 2020
The data downloads for two quizes appear to have been updated, meaning that there is no way to come up with the right answer. The course project could use some minor clarifications. I was very difficult to determine what was wanted, and this lead to my having to re-submit twice. The deliverables seemed to be confusing to both me and the graders. Course was difficult because of this kind of confusion, not because of the material.
par Jake T T•
30 mai 2017
Difficult course, I had to complete it over two sessions. I came into the Data Science track with no knowledge of computer language, which has made learning R particularly difficult; however, after the previous classes I am finally able to search for the information I need to complete the assignment. The other reviewers are correct that the final assignment is a doozy - it took me several hours to complete.
par Christoph J•
9 août 2017
I would have given the course 4 stars if it wasn't for the last assignment which relies on other students to review your coursework. I understand that it is difficult to find another way of grading the assignments but the results of the process here are just too subjective and people influence your grade based on their subjective view on things, which I think is just wrong. Otherwise the course was good
par Jo S•
27 janv. 2016
The content in this course is essential, but the delivery is patchy and the course project is hard to complete with just the learning materials provided. Read around the course and visit the data science specialisation wiki for extra information, and work through it at your own pace, rather than that suggested by the course. It's much easier to do this now it's on the new Coursera platform :o)
par Tim j•
31 déc. 2016
decent enough but this is a heavy subject and really it is not that interesting although clearly necessary. I feel maybe it could have been organized better to make it more interesting Also reading some of Haldey wickhams book he deliberately keeps this part of Data Science away from new learners as it can be a bit dreary, so my recommendation would be to do some of the other courses first.
par L M•
8 déc. 2020
Slides are images and cannot copy text or code, same with some of the quiz Qs - cannot copy the code.
Many issues with people not getting expected results with some quiz questions, different systems give different results.
Should be teaching tibble library, not data.table (tibble data frames can be used to pass/receive via pipes)
Audio quality is terrible - needs better recording equipment.
par Bill J•
7 janv. 2020
In weeks two and three, the course presents a list of data format and how to read them into R. I would have preferred a better description on why tidy data sets are considered tidy that included some side-by-side comparisons and downstream effects of untidy data. This would help me evaluate the effort and risk of introducing errors from tidying the data against the benefit of tidying it.
par Daniel P•
24 oct. 2019
good. I like the videos and the assignment. There is cerain redundancy of information. Much of the "new" information was already elaborated in the previous courses of the same specialization. Additionally, the grading system is based on other students whose knolledge may be not beyond the course scope and submitting an inovative solution can mean not passing the course.
par Edward C•
15 févr. 2017
Lectures add very little to what you get simply by looking at the slides on your own. Facilitators are expert biostatisticians, not R programmers, and sometimes their explanations of R functionality is superficial and imprecise. The assignments are rigorous and challenging, however, and if you take the time to go through all of the exercises you will gain valuable knowledge.
par Youssuf A•
22 avr. 2020
The theory is explained well and there is not much of a problem to follow the content. But there is a huge gap between understanding the theory and applying it practically. After one finished all lessons one is just not well enough prepared to solve the assignments. The problems, which one faces, are far too difficult to address without previous knowledge / experience.
par Alexis C•
12 oct. 2018
first two week need an update, because many thing on the videos dint work easy on the computer, is not bad to look for more information about the subject on the web, but at least made that the examples on the videos work fine went anybody run the scripts on theirs computers, last two week are good a brief summary of R, and how to work with data, love those 2 weeks
par Andrew M T•
25 oct. 2017
The course fits nicely in the specialisation, and I enjoyed the Swirl exercises, which are massively useful. The structure, though, is a bit chaotic, with loads of topics touched only briefly. Perhaps less is good here. Also, I found that the Swirl exercises were repeated across Weeks, and sometimes they didn't have codes to earn extra credits.
31 mai 2016
Peer reviewed assessment with students who are unsure of the correct answers = unsure if solution is correct. Perhaps a formal process (same as previous course where a SHA commit is submitted and source is automatically downloaded (and plagiarism detected) & run to verify the output that columns / data meet an acceptable criteria
par Tareq R•
22 oct. 2018
I think some concepts could have been taught better with simple examples first, and then gradually move to more complex ones, but using noisy data blur the learning objective , and again... the instructors are just showing up a slide.. I think the power of video and illustrations could have been better utilized
par Cameron L•
22 avr. 2022
The course introduces many good packages and skillsets, but doesn't really instruct on their use. The work typically requires extra outside learning to complete. The R courses in the data track have typically been much better than this specific instance, but it is required to complete that track.
par Allyson D d L•
5 nov. 2021
The course is good to learn more R commands but only in the last week there is a practical assignment. I think if all weeks could have practical assignments this course would be excellent. In this assignment we don't use all the commands that we learnt. So, this course has a lot to improve.
par Debjit C•
6 juil. 2020
I had a very interesting experience in the course. Thanks to all the help from the discussion forum and data science communities such as StackOverflow . They have the best resources to learn.
The assignments were a bit difficult to understand but once understood, it was quiet easy to solve.
par Bruno V•
22 juin 2020
The course is good but should update its links and go deep into the regex syntax. Moreover, the tasks of the assignment was not difficult. However, it was not easy to understand the tasks as they were not well explained/written. Overall the course is good and I recommend it.
par Maurice A•
13 févr. 2022
There is a huge gap between what is touched on in the lectures and the project. The upside is that it shines light on what the student should do further research and study on. The downside is it almost becomes unwise and a waste of time to continue with Coursera.
par Ryan B•
20 avr. 2020
Learned some very useful skills, but I found that some of the weeks moved too quickly without sufficiently explaining the background information required (as someone without a data science background) with abstract concepts that were not grounded in application.
par FARROUK_ABDERRAHIM B•
12 oct. 2020
the assignment project was hard and really not enough instruction was given and it was a machine learning data set which made it very hard :) i mean we hadnt seen anythin similar to that during courses :) fix that and change project assignment for final week