(243 Reviews)
(1056 Reviews)
LR
Sep 7, 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.
SF
Apr 14, 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 UJWAL S S
•May 29, 2020
Automated lecture are made using difficult english to understand, it feels like that robot keeps speaking continously without a stop and also the presentations in the videos makes me feel sleepy, if you use facecam that would be better for the learners but not for you i understand that. This course is little far from perfection.
By Sandra V
•Sep 21, 2020
The content was clear and easy the first three weeks. But it was confused to me at 4h week and for the final presentation it was a lack of clear instructions, I was so sad because I had many troubles at the moment of commit, push and fork a file, I had to find external help and I thought I couldn't finish succesful the course
By JAVIER D L R A
•May 21, 2020
Excellent Course, very simple to understand and concisius. If you wish to learn data science and you dont have any idea about it this this is your course. Only the part of Git I wouldl like to be more explicit, because in one part there is not very clear how we have to create a text file with extension .md using Github. Thanks
By Ross B
•Feb 10, 2020
Course was pretty good but the later lecture videos go really fast and are hard to keep up with. The main problem I had was when it covered R markdown it made no mention of having a LaTex program to create the pdf, I had to spend some time figuring out how to install and get one working in order to knit the markdown file.
By Jeff M
•Oct 6, 2017
What needs to be made clearer is the need to go looking around the internet for help on the Git to Github work. I can see that one taking some time for students to work thru. On the other hand once students go throw the trouble of doing the research and working with the code/commands a strange thing happens - learning!!
By Miranda C
•Aug 28, 2023
A little out of date, wouldnt be a problem except it made some parts a little harder to figure out. Google was definitely a friend here.
Learning how to do Markdowns could have a little more matter on the subject and more assignments on getting started with it, again, can be supplemented with google. Still a great course!
By Steve
•Feb 1, 2021
Nice, basic introduction to setting up RStudio and Github. I think the idea of using automated lectures is okay EXCEPT that there is no control of pacing. The automated speaker talks far too quickly when explaining steps that we need to follow on our computer. I found myself constantly pausing, rewinding, and replaying.
By Cesar A d S P G
•Aug 14, 2016
Expectations for simply meeting the baseline learning objectives or to outpoint it aren't exactly clear and there are two monitor strings that are far from being clear (15 minute guide on xyz).
Content and evaluations match in requirements. I learned a lot about softwares and databases in with which I can learn and work.
By Chinmoy C P
•Mar 8, 2020
A high level view but very helpful for someone starting their Data Science journey. Good overall coverage of basics that helps in building a gradual understanding of the subject.
The only reason i haven't rated 5 stars is because there were lot of errors that i came across in the automated diction that need correction.
By Muneeb S
•Feb 15, 2020
Organization of course was good. Sometimes, I felt that speed of the lecture is fast and I had to reduce the speed to 0.75% to understand important concepts. Improvements can be made in the transalation of text by robot, 'e-g' was being translated to EG instead of for example. Overall the content of the course was good
By John G
•Oct 8, 2024
Good foundations course on the elements of R programming. It seems that there is very little engagement from instructors and the AI voice, while entertaining at times, is dated and there are much smoother AI voices that could be implemented. All in all, a good course and essential for the Data Science Specialization
By Xuan L
•Jan 13, 2016
A brief introduction and overview of data science and the specialization from JHU. It provides necessary information and materials for the following courses, but itself does not cover much technique details. Won't take long to accomplish but still necessary if you don't know Git, Github or background of data science.
By Jan-Frieder H
•May 12, 2018
very basic when you have at least some science background in terms of a Bachelor + almost Master Sc. degree, but good for repetition, Git Bash and Github was completely new to me, at the moment I am not 100% sure for what Github and Git Bash are useful, but I am sure I will figure it out in the upcoming courses :)
By Vignesh v
•Jun 26, 2020
It was good and it helped me to explore github,git,R and Rstudio. The peer assignment was quite good as it was my first peer assignment..But,only thing is that instead of this format(using AI),U can use on-person teaching which will be good and interactive..
I felt sleepy with the crampy female robotic voice
By Kendra S
•Feb 15, 2021
The peer review is very subjective. The last question on the grading rubric is about whether or not the work was done by the student who submitted the assignment. One person said yes and another said no, so I only got half a point. I would have had 100% if not for this. I can prove that the work is my own.
By Deleted A
•Jul 22, 2020
Found that the automated lecture didn’t deliver the message as well as a traditional lecture. There was awkward delivery in terms of speech and phrasing from the automated lecture and I found it distracting. But the material was great and I feel prepared to start the rest of the data science specialization
By Harris W
•Apr 29, 2020
The course overall has been helpful in getting started with R and data science as a method of analysis. But the robot voice is extremely difficult to listen to. To the point where I am drifting off because it is so monotone, and sometimes not interpreting the content correctly due to a weird pronunciation.
By Matheus d M d A
•Aug 28, 2018
The course is pretty interesting, but there is not much substantive knowledge here. For that you must keep going to the other courses of the Specialization such as R Programming and the others. There you are going to learn data science in practice. Nevertheless, this is a good introduction to the topic.
By ANEETA B K A
•Oct 15, 2022
The course helps me understand more about data science. However, I see that when instructing using an automated voice, the way the course was taught was kind of boring. I took longer time to digest and complete this course. Hope that in future to variate the voice, not just specific to one voice tone.
By Ian M
•Apr 1, 2017
Good course, that brings goos insights on the basics about data science.
The lectures about Git and GitHub are not so clear - maybe this classes would better fit when the class already have a more advanced knowldge on the course's theme.
Thank you for the quality of the lessons and to make it available.
By Antony S
•Apr 28, 2017
A good place to start of your entry to Data Science. You get to know what data science is, what are the tools used and get an idea of what can be done and cannot be done. The course even walks you through installation of r, rstudio, and git. It introduces version control system using Github too.
By Dawn K
•Mar 3, 2020
I really wish there were a few videos with real people in them. That computer voice is annoying, but the material was covered thoroughly, and I used the text option which actually was great. I also think it would benefit students if there was a book or some form of notes they could download.
By sachin s
•Dec 26, 2019
A Good introduction to data analysis theory and tutorials on getting started with Rstudio and git installation and initial usage techniques. Consecutive course to compliment this would be R programming and Data cleansing and exploratory analysis as in John Hopkins Data Science Specialization
By Syed M R A
•Jun 1, 2017
Very good stuff relating to Data scientist's entrance in the Data Science field but it should be more descriptive in terms of basic tools and softwares like git and github. Although the stuff is available over the internet but when you listen & see, you get more and more efficiently. Thanks,