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.
Apr 15, 2020
As a business student from Bangladesh who is aspiring to be a data analyst in near future, I love this course very much. The quizzes and assessments were the places to check how much I exactly learnt.
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 Alejandro M•
May 11, 2020
Some parts are good, but the presentations are something very boring because the fact that are 'automated'. Is the first course and the concepts are very basic and sometimes well explained but i expected a more interactive course. 3.5 / 5, maybe 8 / 10.
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.
by Sunny S•
Apr 04, 2017
This course is a good start to give an overview on the toolbox you should be aware of to specialize in Data science or analysis. You don't need 4 weeks to complete to complete it! At best you could complete within a week or 2 days. Best of luck!
by Nicolas B P•
Oct 04, 2018
Not sure if we will come back to Git, but i thought that in this section it was covered way too superficially. Maybe the idea was that we should get to it ourselves, but i guess my expectation was different. Other than that, the course was ok.
by Oscar C M•
Jan 17, 2017
Some video explanations are not so clear, so will be great to highlight some concepts, theory and methods or technical (with some reading), also will be great exhibit the latest news about toolbox(that is the current topic) or data scientists
by Rosina P•
Aug 12, 2017
This course stays on the surface and doesn't delve too deep, probably in order to not scare off people who are new to the subject. From what I've seen in the second course, the material becomes a lot more difficult, which I was glad to see.
by Francois B•
May 29, 2016
Would have like to jump straight to the material. Although I understand some may need it, the command line course was pretty basic. This course on it's own doesn't give much. One can get started with Programming with R w/o missing too much.
by Richard B•
May 26, 2017
Fairly basic course covering the fundamentals, I would suggest to most people to complete this course concurrently with the R programming course or to complete it all in one go, as I personally completed it within a couple of hours or so.