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.
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.
by Shawn L•
I think this course could have been done in two 30 minute videos. It jumps in with overviews but contain some high level items you won't understand until later in the course and it really doesn't give enough context to be meaningful at the time. It almost seems like it was thrown together without a clear mission of what should be in the introduction. Being a developer who has used some of the tools in this toolbox (Git, GitHub, Command Line) this intro really glosses over tools that most non developers won't understand. If I had a say in what should be in this intro it would be one video end to end of setting up the tools. The second video would be all about the history and logic and some of the applications we will be exploring without all the r formulas in the slides.
by Susan C•
The automated voiceover is really unpleasant, it's like listening to a phone menu over and over again. If you choose not to listen to the voiceover, it's like reading a book, so one might as well buy a good book or read through some documentation on the web.
My other problem with the course is that it contains a fair amount of subjective information. For example, the quiz might have a question like 'What are the most important characteristics of a data scientist'. Well, the answer to that is obviously subjective, and other data scientists might have a different list.
That said, on the plus side it worked as a quick introduction to the R toolbox, the R/git connection options, and R markdown, all of which were very useful.
by Florence C•
I wonder if most of students who took this course would have expected to keep installing software, one after one, and seems never end. For the whole week course, there is nothing related to the course. The worst part is that once I got into trouble to get a software work, I got stuck. I spent hours and hours to search for a solution on the web. However, most of the answers are too technical for me to understand. Although I understand that data scientists have to use some computer programs to assist them to the work done, I don't think that, as a novice, at this stage I need anything like GitHub or git to help me learn the subject. Moreover, I'm not sure if I would be taking another data science course yet.
by Cándido O M•
Too basic material. I had to watch everything x2 faster to keep my attention. This course is just an overview of the topics that will be explained in the next courses, which could be much shorter in just one lesson or just avoid it. It is only useful to have R and RStudio installed and to introduce you to GitHub if you do not already know it.
I think it would be an improvement if you made the videos longer in order not to repeat yourself. Because sometimes you are constantly reintroducing a topic and never getting to explain much of it.
I hope that the rest of the courses be more direct and easier to keep my attention on, because I really what to learn about this subject!
by Julia S•
This is really bad structured. As analogy - imagine a cookbook that says: in chapter 8 you'll learn how to cook pies, but here in introduction let me tell you how to add baking powder in them. It is not useless information it is just very out of place.
Learning some git commands was the most usefull in the course for me. (Though again, why would you first show the commands and just after that explain what's the tool for and where to download it?)
And by the way it takes half a day to cover all 4 weeks material (listened through all videos, installed the tools). Which is not bad, just of other users information)
by Lenka " P•
It is a nice introduction, but the material is barely worth a one week orientation lesson (I have completed it in about 4 hours with watching all the videos, taking detailed notes, and completing the quizzes for 100%) . Why should we pay for a specialization preview, we can read info about the separate courses on our own? I am really looking forward the next courses in the specialization since I really need a good course in statistics and this seems to be one, but the first course should be eliminated. The installation of R and github account videos should be added to the next course as an introduction.
by Claude R•
A very big difficulty for me, French people who uses to speak english everyday for may work is that :
1) Teachers don't do any effort to speak slower and to articulate
2) As with all other courses I've attended in english, it's impossible to read a transcription, even in english, while reading slides.
Slides are not sufficient by themselves, i.e. without commentary beside. The workaround I've used is to print french translation or english transcription and read it, trying to guess which part suited with which slide ...
For a non-free course, it's not really professional ...
by Shannon P•
This was a very basic introduction to the concepts of Data Science and the software necessary for future courses. Overall it was fine, but I hated the robotic voiced over videos and ended up playing them on mute to get through them. Also, considering the explanation that doing the class content and videos this way allows the university to keep the courses up to date and correct errors faster, there was still several out of date sections of the course and a number of errors, so I really felt the automated setup as an annoyance not a benefit.
by Amy-Louise S•
The lectures were very uninspiring for the most part and I felt that my practical understanding was poor. The forums were also not particularly helpful as I saw Moderators mocking students for asking valid questions based on their inexperience with Data Science. I spent most of my time finding better tutorials on YouTube, so really.... I have a certificate but don't owe much of that to the course in question. It only becomes worse as the complexity increases with the rest of the Courses in this Specialization.
by Sem O•
The course is well structured and provides a good introduction, however, I expected a bit more from a course that costs 20 pounds than just a few clips on how to install and set-up software/create a github account etc. This information is available for free online on the websites of the respective software.
I understand that such an introduction is needed for the course, but then do not offer it as a separate 20 pound module. Instead include it for free with any other 'specialization' you can buy.
by Pradeep M V•
It shouldn't have been a stand alone course. Just a series of "how-to" instructions on installing RStudio, linking it with and using Github, which one can find easily with more detailed examples on the internet. This one can be combined with the next course (R Programming) in the specialization.
That monotonous bot voice is a serious drawback. Having an instructor is very important for a course especially when you are charging for the course.
by Tarik G•
I can see that the lecturer's intention was giving an overview by mentioning all different topics, however, it just got me confused. I wish it was more into a solo topic. It would be great if it was given only git/github lectures, so we, the students, can be more comfortable when it comes to uploading files in the following courses. I see in the discussion forums that github is a problem for most of the students in the forums.
by Sydney R•
You will learn some command line commands, git, Github, and what you will need for eventually using R.
I was a bit annoyed that this course is a requirement for the Data Science certification (which is what I am after). I already knew everything taught in this course and was a bit annoyed I had to pay for this in order to complete the track. I think 'courses' that are fully setup like this should be opt-in not required.
by Ignacio S U•
The course is extremely introductory and even though it may lead you to references you may use to self-teach yourself, it is not worth taking a four weeks course for a one week content. At the end of the week you will have about three new programs installed in your computer and no idea on how to use them for practical situations. Although it's intended as introductory, it surpasses that barrier to mere spectacle.
by Daniel P•
In my opinion, the content of this course is too basic and little bit of topic for the data scientist specialization. Of course it is useful to be able to use git, shell etc. but I believe that most of the people already know those and the rest of the students can be redirected to relevant study material. All in all, there was about 90 minutes of relevant study in this course.
by Ben V•
Very very introductory. I didn't find the tooling aspects of this course particularly helpful, but I'm not in the target audience. It's length was misleading -- I completed the work in two days easily, but I am a technologist, and already had the tools installed. If you use GitHub and RStudio, the meat of the course is only about an hour of the lecture.
by Daniel J R•
Not very engaging videos. Superficial introduction to the mechanics of some tools without providing much context. Final submission did not work per video explanation. Need a more engaging presenter. Not quite at the level of Prof. Ng's Machine Learning course which I realy enjoyed and learned a lot from.
by Heather G•
This should not be its own course, as it would be pretty useless if you were doing it on its own without doing any of the other courses. The end of course project literally being just to make a Github account and download R-studio could be quickly covered in the first week of the other courses.
by Raj K P•
showing - doing things live in the video would have been great .. it seemed like explaining a PPT by an instructor. You could have taken one data set and have done all shorts of things and then in the midst thrown some quizzes to student instead of going though all the discussion in one go
by Lyn S•
Not bad, but certainly not good. I cannot believe there is a style of teaching where you never get to see the best way to do something. I can slog thru the programming, but I doubt it's the best way to do something, but I never get to see how something should have been done.
by Eric J S•
Very basic course. Poorly motivated, material presented without an effort to demonstrate why. This is not entirely out of place in this intro course, but it permeates the entire program. Difficulty poorly controlled, projects and quizzes much more advanced than lectures.
by George C•
I personally think that this course should be better interwoven into the other modules of the data science specialization. It's a quick primer, but aside from that, not very valuable in terms of the information that it's providing. I wouldn't pay $49 to take this course.
by Thiên P T N•
It is generally good course, but I feel it is just a combination of very little tools, skills and ideas. I believe it is quite complicate for me to understand, esspecial git and github. I think it is better to combine this course in other courses where you need it.
Would like to have seen more material using git and git bash commands. More repetition here would be helpful. Seems like a lot of this information will be lost or forgotten when it comes time to use it, especially as it relates to the Data Science Specialization.
by Desabandhu P•
Course content is good. But the way of teaching is worst. It is because of the computer generated voice i.e text is converted to audio and we listen to that. It is extremely horrible and feeling bad without any interest. Natural way of teaching is best.