Es un muy buen curso teórico práctico sobre la introducción a la programación en R. Sin embargo algo que me desmotiva mucho es que las constancias no proporciones ningún crédito. Éste curso tiene una duración de 4 semanas y no me parece justo que no se tome en cuenta si se acreditó correctamente no tenga ningún valor. es el segundo curso que tomo en coursera y me pasa lo mismo, dos universidades renombradas que no proporcionan ningún crédito. ¿Cuál es el objetivo de ofertarlo entonces?

This course is a pretty good introduction to data science, although I'm not sure how useful will these tools be in the future. Also many tutorials were unclear and I had a lot of problems to complete some lessons, I had to solve those problems by myself (difficult task sometimes considering I know nothing about data science). Anyway, I learnt a lot of stuff, in the end I recommend this course for an introduction, but check the content first to see if it suits your learning interests.

On enrolling in this course I knew nothing of the data science world and always wondered how all that "jumble" of data was organized. After this short course I am beginning to have a glimmer of how this is done. I know there is more to learn and I am curious to know how. I must say that I struggled quit bit towards the end of the course with the assignment, but I believe as I continue with the other courses I would become more proficient using RStudio and GitHub, etc.

Important note: Opt for this course only if you plan to do the entire specialisation. It is developed solely for the specialisation and not as a standalone course. You will not learn much concepts or theories or practice any R programming here.

In general this course was basic and good enough to get someone started for specialisation. The video lectures for setting up R, RStudio and Github account were helpful and very basic ( maybe coz I already had an github account).

A lot easier than I expected. In particular, compared with courses where one final project took longer than the rest put together, the project is just to put together your R/Rstudio/Github environment. There's a lot of value in that; plenty of courses you learn the concepts but don't add anything to your repertoire; if a year from now I'm putting a little project on my new Github site now and then, that will have more actual impact than a lot of harder ones.

I wish this course would spend a little more time upfront saying at a very basic level what data science is and gave some real life examples of data science in action. Most of the course is configuring software that you will be using down the road, but it would help to know why you'd even want to use the programs and in general, what their capabilities are before you get into setting them up. I imagine that latter course will do this (at least I am hoping).

I would have preferred more hands-on examples or projects for each week's lessons. As an entry-level practioner, this course felt thin. There is a lot to absorb and while my interest and curiosity is peaked, I've found I've been able to grasp a better understanding of the material outside the classroom rather than inside. This is ultimately a good thing but I would have liked some more "hand-holding" from the course to feel more comfortable moving forward

Great intro course. Got me in the mood, established the perspective I should have going in. I felt that some of the lessons like R Markdown were a bit rushed and at times I felt like I was just following along without understanding the commands and ideas. Perhaps that is simply a lack of emphasis or a lenience in tone (like don't worry, we'll look at this later), but in that case I suppose it did not translate through the synthesized speech.

Although the modules were supposed to teach us how to setup RStudio and git on our computer, I found that they ignored a lot of common errors. I use a Mac and had to spend a lot of time on the web looking for answers to how to complete the setup. Although the solutions were pretty straightforward, it took me a long time to identify and pick the right solutions, the right downloads and the right tweaks to get everything going.

It's a very introductory course where topics are only mildly touched upon but I guess this is the goal of it. Git, GitHub, R, RStudio, Statistics... these are only signalized and very basic introduction is given. I trust that the other courses that constitute the whole Data Science Specialization series will dive deeper into the individual subjects. But as an introductory course The Data Scientist's Toolbox is OK.

This is an introductory course to the vast theme of Data Science. Fundamental concepts in data science are given and also the access to the most commonly used tools is showned, as its name suggests. You'll learn which questions to ask and how to answer them. You will setup your data science lab in your pc (R Studio) and join the development community using GitHub. An entry-level well paced intro course. Very Nice.

The narration and content are excellent. The clarity of the slides has given neat direction steps to installing the software. However, I was not able to co relate the Command Line Interface with the R console after installation. I was overwhelmed with the huge list of commands immediately after software installation. The Week 3 videos have great information but lacks a coherence with the remaining course content.

This course was a very good broad overview of what data science is. I've taken some courses tangential to the topic before, so it wasn't particularly groundbreaking. I encountered some complications installing software and didn't feel like it was always very clear what order I was supposed to do things in, and I wished I'd had more help since installation issues can crop up much later and be hard to diagnose.

The text to voice simulation needs work. In particular, it does not understand that when the word "record" is used as a noun, the accent is on the first syllable. When it is used as a verb, the accent is on the second. Also, to output markdown files to pdf, on Windows machines, one needs MikTex and to link it to R Studio. I have downloaded MikTex, but I have not yet figured out how to link it to R Studio.

The content of the course is very detailed, including a step-by-step guide which really supports beginner as the course promoted. The pace of the course is just right. The only comment here is that, while I understand the underlying reason, the course is presented using a text-to-speech voice which makes the course sleep inducing. Overall, it is a good starting point for those who want to learn R without a

I like the structure of this course, it introduces essential tools for people who just begin the journey of becoming a programmer. But the quality of videos needs to be improved, especially the instructor is basically reading the slides and sometimes it's distractive and boring... Besides, I don't think it's necessary to include all the introduction sessions for other courses in this specialization.

Are you ever tired of long-winded professors, focusing on a lecture for an hour or more, or spending hundreds of dollars for a class? Well, this course gives you the structure of a course, but the video lectures are concise, the topic is to get you in the door for data science, and all of my questions were answered quickly. I recommend this course to anyone who wants to learn data science.

Great Course. But for completely beginner who don't have any previous experience in programming or have some experience in other language but not in R Programming Language, might find it difficult to cope with. Many of the times, I had to google and watch other tutorials in youtube to understand some lessons of the course. Besides the artificial robotic voice was quite irritating for me.

I get that this course is more catered towards people who have never set u Softwares like Matlab and Rstudio and have never utilized Git but I felt that if you are touching these topics, it would be better if it were more in-depth and made a strong foundation. The statistics part of the course was excellent but I felt more attention could be paid towards Git and Bash and CLI in general.

Good introduction to the Data Science Toolbox. I found the course very engaging, setting up foundation for further studies. Also, very nice platform supporting In my mind though taking this course makes sense only in conjunction with the other courses within this Data Science Specialisation as knowledge you get from Data Science Toolbox will not be sufficient for practical application.

This is my first course on Coursera so i didn't really know what to expect and i apologise in advance for my ignorance. The course only takes you 8-10 hours or so to complete (even accounting for reading external sources) spread across a month. I enjoyed it, but it doesn't feel like i learned a lot and i think it could be a bit more intensive. Looking forward to R Programming (part 2)

With my background, this was a lot of repetition of information that I already knew, but I was happy to have the reminder and it would be an excellent introduction to the specialization for others who do not have as much of a data science/computer science background. Overall, I feel that the course was worth my time and I feel it is a great start to the Data Science Specialization.

It is really a long journey to me ( 6 months) and the study itself has ups and downs. The first few courses and last few course are relatively easy to me, but the statistics reference and regression model are really hard, and Capstone is the most difficult and time consuming course/project ever. I am glad it is all over and pretty sure it will be worth the time and effort on it.

I found it somewhat difficult to figure out how to push the file to GitHub (luckily I did find some helpful info in the forums), but otherwise found most of this course very easy. I can't figure out how to download the slides in such a way that allows me to click on hyperlinks, and I've seen other people say the same thing. I also would recommend making the code & graphs bigger.

Overall, a great course. However, I hope that the course will focus on explaining overview and principles of data science, not explaining how to use toolboxes such as R and Github. R and Github as data science toolboxes are not well explained in this course because it was quite difficult for me to follow what the instructor showed in his slides about using codes in R or Github.