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 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 13, 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 Anthony C•
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 Ian M•
Apr 01, 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 B•
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 M K•
Mar 03, 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 01, 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,
By Marco L•
Feb 05, 2017
It was a little to easy and the quizzes were not really necessary. Questions like "What courses are in the Data-Science Specialization?" don't help to controll my learning progress. However for a first, introducing course it was okay. R Programming is way more interesting and challenging <3
By Ziaur R•
Dec 20, 2019
Didnt enjoy the voice on the automated videos, but was faster at reading than watching videos. The document didnt work for the Big data Section and had to watch the video for this. Good introduction and wished I had more questions to practice! Looking forward to R Programming section next"
By Glauco G d A•
Jan 11, 2018
It's a good start point for people who wants to start pursuing a data science career and haven't a statistical background. Explain the basic definitions of research analysis types and shows the very beginning of handful tools like how a git repository works and good editors for R scripts.
By Marek B•
Mar 11, 2018
The course is very basic but still contains useful information both on data science and some of the tools.
Unfortunately, because of how basic it is, I found the quizes focusing on trivial and subjective questions that are both hard to answer and not really testing any interesting skills.
By Candice A M J•
Jan 24, 2020
The tools needed are all explained well, including installation. Still getting used to the new Amazon Polly format. A few questions in quizzes seem to not align with updated material, but that could just be an intentional push to be resourceful. Looking forward to the next course.
By Sarah G•
Sep 06, 2017
Overall a really nice course for looking into Data Science. I would've liked more on the general field of what is data science and what kinds of problems you might solve, etc. But the lectures were good and the timing was very manageable for working professionals to do. Thank you!
By Alberto H A•
May 19, 2016
I found this course to have very useful material and good, clear explanations. My only criticism is that the last of the four weeks has practically no content. There are no lectures and the only assignment is grading the assignments of other students, which at most takes 20 minutes.
By Lee K•
Jun 29, 2020
The part on how GitHub works (Including the Git Bash) section could be further discussed for a better understanding of how to use the platform. Overall it's a good course! well structure. just that content could be more detailed so that it will be a even more meaningful course :)
By Figo C•
Dec 04, 2017
Great learning on the basics of Data Science and it's importance in real-world applications. Help to get started with introduction to Python, R Language, Git!
Lectures could perhaps be more engaging and have more visual appeals (instead of having just lots of words on most slides)
By Guilherme B D J•
Feb 16, 2016
This course is good to get all your programs set up before you start your studies in Data Science.
I think it could offer a little bit deeper knowledge of git and github in order to guarantee it will not be a problem later, since they will not be strictly related to data science.
By Eugenia G•
Jan 22, 2016
The course content is very useful, but explanations are short and It's unclear how to install R studio for the Windows (I found it at Youtube). Also I had a problem how to install the R packages, and solution was simple: you should run it as administrator (it wasn't in lecture).
By Ximena L R•
Mar 31, 2020
I felt like I was able to keep up with the course material fairly well. My only critique would be when it comes to using git, the commands aren't very intuitive to me. Maybe explaining the commands a bit more would be more helpful, i.e. what the commands are telling git to do.
By Rahul P•
Jan 25, 2017
Very nice introduction! Unlike a lot of online courses, this course is no fluff or jargon. It is solid stuff with hands on experience. I only wished this course was longer. After completing the 10-week Machine Learning course by Andrew Ng, this course felt a bit too short. :-)
By Colin L•
Mar 31, 2020
Very basic. A few tweaks are needed in the last quiz's questions - the one pertaining creation of a .md vs. a .rmd file, and how to make sure the "## " prefix is properly given. (There should be a space after, and graders need to look at the raw file, not the presented view.)