Sep 08, 2017
It was really insightful, coming from knowing almost nothing about statistics or experimental design, it was easy to understand while not feeling shallow. Just the right amount of information density.
Jul 22, 2017
Great Primer for what Data Science is about. It also provides the infrastructure of tools needed. This was what I was after, a way to provide other data scientist hardware and infrastructure support.
by Jairaj A P•
Aug 26, 2019
i felt this course was very disorganized. It introduces terms and concepts not explained before. There was an assignment on creating forks. This process was not in any lecture. Of course, with R and GitHub you can find anything on internet.
The lectures narrated by Amazon Polly is very boring. It also messes up some of the terms.
by Lou O•
Jun 21, 2016
It's ok. After the first lesson, I should be able to provide a clear elevator pitch with a high level understanding of what I can expect to accomplish (4 or 5 steps) as a Data Scientist. Instead, there was one slide that touched on this quickly, somewhere in the middle. What are the problems, how do I solve them, give samples.
by Sandro G•
Sep 21, 2016
The first course is composed in articulate way that allows a simple and schematic way of comprehension, but some single parts of the first course seem to be lacking of some information, above all to me without previous experience in informatics tools like github. Maybe I suppose to master this tools too long in advance.
by Ashok N•
Mar 13, 2019
literally i lossed the feeling of real time learning and it seems like just reading. i really do not like this kind of teaching style. infact direct teaching by the instructors is being a good experience rather than using this kind of technology
i reas all the course content, without listening by recorded speech
by STEVEN V D•
Nov 15, 2017
Good introductory course for the specialization.
Video' probably need an update as they're all cut in the end.
Also some more background and a little more extensive lectures would have been nice.
Anyhow, it did the deal: an introduction to R, RStudio, Github and Git.
Curious what the following courses have to offer.
by Nguyen N T•
Jan 21, 2020
The course size is pretty small compared to other courses I joined in Coursera. It took me only 3 days to complete the 3 day course. I think all setup guides should be left as assignments for students with some links where we can refer to on our own. Anyway, the course finally convinced me to start using R.
by Mohamed H•
Dec 15, 2016
Instructor speaks very fast so that i read subtitles instead of hearing what he say, in addition to i stop video more times to understand what he say, but totally the scientific and technical contents are great also his advises for us in which how we can find the answers for our questions about data science
by Jose O•
Feb 06, 2016
The part of explaining Predictive and Inferential Analysis is confusing. I think it won't hurt to give some more specific examples and methods used in each case. Both types of analyses involve sampling, so I think it is necessary to keep it clear how that sample can be used to either "infer" or "predict".
by Fred P•
Mar 06, 2016
the lectures are full of the Prof misspeaking, this leads to you not knowing how to complete the task because the Prof can NOT communicate properly to us while we listening to the lectures... it seems like they completely missed the fact that NONE of us are data scientists...
Now your Audience......
by Alejandro O•
Dec 17, 2017
I put three stars because it should be specified more how basic this course is, is almost that this is done for somebody that doesn't know almost anything from CS. So it should integrated with other kind of specialization. I hope that the following courses have some serious math and advance topics.
by Donald J•
Nov 12, 2016
The course goes over the basic toolkit for data scientists. Overall it seemed too easy and maybe a bit simplistic. I was expecting more. There was a lot of optional reading made available in week 1, perhaps some optional assignments/quizzes related to that reading could be added to the course.
by Diego N L•
Sep 23, 2016
concepts were very good but teaching method/material must improve...some of the materials and methods used are too unstable to be useful for professional use...more work should be done by instructors to separate the 'reliable' concepts/info from 'interesting to know but not ready for mass use'
by Steven M•
Feb 12, 2016
Very basic material, but a good introduction and a necessary step to ensure a baseline of knowledge for future courses in the data science specialization. I would only take this course if you are interested in the specialization otherwise save your money and google the info you need.
by Noah M•
Feb 11, 2016
Insufficient available project available for review and thus unable to pass course due to technicality. This is a major problem. The course should still be passable even in the absence of sufficient other projects to review, which is a problem that no student has any control over.
by Dane S•
Sep 08, 2017
I was a little put off by having to grade my peers and it felt like the final task required a few bits of information that hadn't been previously covered. I felt some more examples could be useful in getting people adjusted to GIT. Not a bad first course but not what I expected.
by Luis C•
Apr 29, 2016
The materials are good, but it felt like this class should have a been a 1-week introductory lesson to Data Science. It is definitely now a 4-week class, maybe a a 2-week one if you take very easy. You end up with a basic setup for the next class. That I found very useful.
by Sharon F•
Feb 15, 2016
Very light & not really consistent with the heavy workload of subsequent courses. Felt it could have been much much stronger explaining GitHub- which shows up as a problem in latter courses strongly suggesting that toolbox does not effectively cover GitHub for newbies
by E. G F•
Jan 27, 2018
One thing to note, I am using a work computer, so our IT support had to add the software required. This was inconvenient for them because I had to put in several support requests as I progressed through the course even though I installed as much as I was allowed to.
by Pedro V Q d C•
Sep 27, 2016
I think the course was too superficial and didn't cover enough topics to be a standalone course. It could be part of a greater course. My feeling is that this wasn't worth $30 dollars, and that such a small course was put together just to charge for one more module.
by Ryan W•
Aug 21, 2018
As an intro, this course is probably pretty good. I, however, already had experience with R (although the refresher was useful). However, if you've taking a data science or machine learning course recently, I'd give this one a pass and head on to the next course.
by Shady E•
Nov 12, 2016
Thank you for the fantastic effort. Here's some constructive feedback on the course.
It's a very basic course, could have included more material. Also, the audio quality is not that great. To make it better, I'd Include more walkthroughs for Git and GitHub.
by Diego L•
Mar 08, 2017
Too little substance, though I do expect the rest of the series to be good as I take this as a setup course and my expectations for those are high. Having said that, perhaps it would be wise to charge less for this initial course or even offer it for free.
by James M•
Dec 01, 2016
Really tough to review this class outside of the context of the other elements of the data scientist specialization. What was presented was straight-forward and quite well done. After I know how well prepared we are for next classes, I will re-evaluate.
by Ced W•
Apr 20, 2016
This is a course to get you set up with all of the tools that you will need to go forward. No hard homework, but you will be ready to work. The intros into various aspects of the curriculum also serve to prepare you mentally for the coming weeks.
by Vasilis S•
Feb 18, 2016
Useful steps for starting the specialisation, but should this really be a course that people are paying for? Come on guys. By the way, some R programming concepts could be introduced here and de-clutter the congested/crammed R programming course.