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I'm doing this training for the second time, now as a beta-tester. Particular comments about lecture content, problems, etc. have been put in every lecture.

General comments, in short:

1) Related to the new platform and UI design:

_ It is cleaner and simpler than the previous one. I like it, BUT...

_ It lacks of some useful features: saving intermediate results in quizzes before submit them; calendar; limited number of subforums.

_ The most relevant flaw: there are not downloadable versions of lecture slides. Unacceptable! No way to check most of the links we saw in slides (URLs not visible).

_ Description and steps in course project appear "too packed" together. I prefer the former design.

2) Related to content:

_ The course is mainly for preparing students for the rest of data science specialization program. When you said "toolbox" you mean the concrete toolbox you will need to do the program. Some people expect to have a general introduction to data science but that is only a half of the content. I think this is clear enough in the presentation but for some reasons there are people in forums who protest the content, so maybe you should insist more in this fact.

_ I would like to suggest some kind of reorder of material: week 2 is all about installing a running tools and week 3 about key aspects of data analysis. Maybe you can split both types of content between wk2 and wk3 to make wk2 more appealing for not technical oriented students.

_ Git is a source of problems for a good portion of people. See my comments in lectures about how Git is explained.

So why not 5 *'s - because I could not give 4.93 *'s

What I found excellent and thus 5 *'s - *****

a) material

b) even the automaton of a voice - was not a deterrent but rather soothing - oh well tells you something about me!!!

c) Material is deceptively easy in the front end and gets progressively more difficult later

d) the references are well thought out even of the entry into Data science

e) The build up is very logical

f) Lots of thought has been put into the design by the team

What I found lacking and thus dinged a couple of points (perhaps too harshly)

1) The mini quizzes do not sufficiently force you to think about the material and thus easy to breeze thru to the next week - perhaps I am being too judgmental and it may improve in the next course of the specialization

2) I could not see an easy way to get the material to review when taking the test - most of the time I forced myself to not look at the material to test myself but the onus is on you

3) It should not be about getting some questions wrong but learn the material so that every question can be right (imparting knowledge vs getting a certificate with 80% pass - think how would we feel if this was a training for a neurosurgeon ;) ) - a suggestion would be to force the student to read the section that pertains to the incorrect answer and not allow the test to be taken again until that is accomplished - like in a class room setting.

This course is a great intro to the potentials of R and the world of Data Science and Big Data as well as the approaches and mindset needed for it. It's fairly straightforward, my only suggestion is to maybe include some tips on troubleshooting some installations for some parts of the lectures. For example, I already had R and Rstudio installed from my college days, but the versions were outdated (R 3.2.3) and weren't compatible with some packages and they weren't working but I wasn't understanding why until I had to google it. Similarly, I had some issues with linking my GitHub account to my Rstudio because the route it was using wasn't working and the correct one was highly similar, I was only able to fix it thanks to forums. These details can be frustrating for someone who's trying to follow along with the lecture but is stuck, so thank god for forums. It would be nice if the instructor could write a couple of tips on how to fix some common issues like those for novices.

I understand the decision to use synthetic voice, but at some point it gets boring and uninteresting because of the lack of variety in the tone and loudness. I still get reminded from time to time that the voice I'm hearing is AI generated. But besides this, the course content is a good way of introducing a beginner in the environments of GitHub, R, and RStudio, although personally I still don't fully master how I to connect these different platforms or languages or interfaces. Particularly, the lesson on commit, push, and pull and how to these tasks are supposed to be done in and across the different platforms (including GitHub) still confuse me. The programming per se does not worry me as much as getting the file paths and directories right and making sure that I am where I am supposed to be whenever I do a task. Lastly, I believe there are some typos in the transcript so please review and make the necessary corrections.

Excellent Course for learning Basics. I had no previous experience with software, computers aside from surfing web, checking e-mails and some Microsoft Office. I'd recommend this course to anyone Interested in data-science or coding in general. The course is easy but not too easy the frustration of dealing with computers exists and I feel it was important for myself to struggle a little bit. I followed the advice of the instructors and sought out solutions to issues. I spent twenty hours a week but if you are tech savvy, take good notes, follow directions and everything goes as planned you could possibly get through the course in a lot less time. There are also a lot of people willing to help. The course shows you how to seek out help efficiently. I didn't request any help this time around had I done so I would have spent half as much time on the course.

I understand that automated videos are a great advantage in terms of updating, editing and that they save time for the team working on this course, but the system has still some flaws; they have no intonation (or intonation is constant) and pauses are not properly made in some cases, difficulting the understanding of some sentences as they're directly linked to the next one without stops or pauses. Also, symbols are pronounced (slashes, etc.). It has no soul, as mediterraneans like me would say, and this demotivates a bit. So still a lot of work on this to make it better and closer to a human voice.

As per the content, being a first contact with DS, I found the course quite complete and well written, although some steps for configuring R Studio or to push files to github repositories where not that clear or were directly missing.

I think this is an excellent course. If I could I would give four and a half stars. The only reason I wouldn't give it 5 stars is because I would prefer to have my work graded by an expert rather than my coursemates. I tried to mark as fairly as possible but didn't know whether I'd done one of the questions properly. So I marked other people down on where I thought I'd made an error (but wasn't sure whether I had or not). I think this could be potentially unfair to people as they may have got it right. If an expert had marked all the work then we would all be sure that the assessments were correct. This is quite a big deal when it comes to confidence in one's own progress moving forward. However, I thought the content was great and easily accessible and I am looking forward to continuing the course.

A good introduction to some of the tools used in data science. However, it felt like the lectures for git were a bit rushed. Also, while it is easy to do so by following the provided instructions for Mac, I have noticed there is no lecture/tutorial for installing RStudio on a Windows System. Overall, I think the course was a good introduction to the 10-course specialisation. Although, as a course in itself, it is somewhat lacking. The provided reference text by Professor Jeff Leek (The Elements of Data Analytic Style) is a concise summation of the course with extra information on best practices. I would recommend all students enrolled to download and read the book twice to get a better understanding of the concepts introduced. Personally, this helped me quite a bit.

I think there could be more lectures on programming related to R. After this course, I am now able to just link any R file(project, script, markdown files) to Github. I also got to learn various features of world's largest repository holder like steps involved in pushing any document to Github repository. Since I am little more interested in leaning the programming languages, so this course did not meet my expectations. Instead it turned out to be some course with greater emphasis on theory and working of the RStudio.

Rest overall, it provided me with the base knowledge of data science. I am sure that this course will cater greatly to my foundation of career as a data scientist. Thank you.

This course was a great introduction to Git Hub, RStudio, version control, and very basic research design principles. It is perfect for Beginners, as is it marketed, so I do not understand the reviews about the course being too basic and easy... that's exactly what it is supposed to be.

The reason for only 4 stars is that the course lectures, particularly in Weeks 3 and 4, are in dire need of updating. For those starting with the most recent version of R Studio, some of the options and syntax needed to complete the exercises were different, making it difficult for some people to complete the assignments. An update and a little more clarification would go a long way to improve the course.

Very basic, but quick to move through course with the minimal needed to setup RStudio and connect with github. I am super familiar with source code management. I haven't used R but the environment was very simple to setup with the instructions given.

I actually did enjoy the general overviews. The first week I didn't bother taking notes but enjoyed. The fourth week I actually got some solid overview information out of and took good notes.

4 stars for the automated lectures. Not bad, but I was SUPER relieved that the next course did not have these. I may have ended my trial. It was definitely minimal, but better than a bunch of useless extra information added in.

It was really good for a beginner's course. I thought that knowing how to code was the only limiting factor when it comes to learning R but this course showed me that as an upcoming Data Scientist, one needs to know what they want to do in R and decide how they want those questions answered. I got to learn far more about Git, Github and R Markdown which I think will really be helpful for the projects that I will set up during my PhD. The course was 4 weeks but I managed to get through it in 3 days. I am especially happy that my request for financial aid came through because without that, I would never have been able to start this course in the first place.

Great review of the foundations of Data Science. I would have also included some background into the basics of database design, table construction, data file content examples from both relational/NoSQL (etc.) sources. Also, would be great to get a compare and contrast of the value of R versus, say, Python-Pandas and VBA because these are the other two free resources out there for handling data in some form (yes, I know VBA has limited applications in deep data science, but it IS still relevant in business analytics---a common launch point for the career of many would-be data scientists). All in all, superb work, folks. More please! Need input! (Johnny 5)

Estoy bastante satisfecho con lo aprendido en este curso inicial del programa Data Science ya que es una buena introducción a todo lo que se verá más adelante. Debo decir como profesional de IT que me ha sorprendido cómo empieza desde cero explicando todo lo necesario para entender e instalar las herramientas informáticas necesarias para el curso con un nivel casi de principiante. Sin embargo en lo relacionado con métodos estadísticos y de análisis de datos el nivel no es tan bajo y los videos de la semana 3 han profundizado ya en algunos conceptos del análisis de datos. Espero que en los siguientes cursos se expliquen detalladamente desde cero.

Overall an interesting program is offered. Just started, an update is expected towards the end of the course. So far found an issue w/ quiz #1 (incorrect grading due a broken internal logic (?) where 2 different 'correct' answers are offered during subsequent quiz sessions). Also, I would say that the intro videos are too short to be useful. Anticipated scope is well aligned w/ modern trends that are re-branded from the underlying concepts known for a long time; such concepts were always being in the arsenal of any serious practicing engineer or scientist. Modern packages though are a nice compact up-to-date tools collection.

This is a very good course for beginners. The tools covered in the lessons and quizzes are indeed vital for contemporary data science. The suggested readings are very helpful and interesting, and the quizzes are also good. The only weak point of this course are the automated video lectures. They are quite boring due to the monotonic, computerized voice. Ok, automated lectures surely have some advantages, as explained by the instructors. But human emotions are key to learning. Thus, to promote accessibility for disabled students, there could be a mix of video lectures taught by human instructors and automated readings.

I would recommend it to any one. The introductory course is so basic that some might see it not important but the course has done a well job by easily getting across the foundation of Data Science as well as helping non-programmers to easily drift into this field. I would have given 5 Star if i was allowed to attempt my tests even though i am auditing the course. The only thing coursera should benefit from is providing the certificate. By freedom of giving test and doing courses people will surely pay for one course or another when they get more confident with their results in audited course.