The Data Scientist’s Toolbox is a great way to dip into Data Science and the methodology behind it. The course is very general, and makes an effort to cover the bigger scope of things without delving deep in any. More than anything, it's a great way to learn the components and uses of data science and set a framework for all that will be coming after.

The materials are very well laid-out and almost feel like attending college classes. The visuals and slides are a little dry, but the pace is lively enough to maintain momentum at all times.

A light introduction to the Data Science field, in many ways it can be difficult for inexperienced people with software or inexperienced with stats - in my case it was not very difficult since some of the topics were already familiar.

The course can be done in a couple of days if the topics are already familiar, in my opinion the course's contents are perfect for someone very new to this field.

I would have rated more stars if the course's content was more "objective" for people unfamiliar with the subject - other than that it's perfect.

I got some bugs when running git bash and I had no clue how to fix them. I kept watching videos over and over but I couldn't find the answer. Then I tried google, reading many websites and doing trial and errors until the errors were gone. I understand in real life this is what I need to do but it would be good to know some proper ways of fixing errors after I submitted the assignment. So I can learn and use in future! Apart from that, the course is really useful and prepares me for the next stage.

It is a very good introductory course for anyone who would like to learn data science, or would like to use tools in data science for their everyday work (like me). In the course you will have some general essential ideas about what is data sciences and which tools are used in the science, and the most importantly, the concept of version control and the GitHub tool for the purpose. However, there will not be any in-depth knowledge in the course, which is determined by the introductory nature of it.

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.

I loved what I learned in the course and I'm convinced that it wiull help me foward my career. I'm giving it only four stars though because of the impersonal robotic-voice videos. I have read your arguments in favour of it, and I'm aware of your limitations, but even though that there are real people behind the voice, I miss a face and a modulated voice and probably even glitches and slips and 'umms' and 'ahs'.

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.

This was a very good introduction, although IO found some of the technical requirements difficult to understand. The AI voice takes a bit of getting used to; just understand that words will be mispronounced periodically. That is not enough to cause any big problems. Hands on practice is the key to get you through. Because this course is a foundation, it goes without saying that you must practice.

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.