Отзывы учащихся о курсе Набор инструментальных средств для специалистов по обработке данных от партнера Университет Джонса Хопкинса

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.

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.

Solid starting class that highlights the fundamental software you will be working with for the Data Science Specialization. It holds your hand at the beginning, but familiarizing yourself with the software may lead to a few bumps in the road.

Some of the issues were due to unclear directions and, at one point, a needed additional package to knit PDFs from RStudio R Markdowns. While the forums are not very active, there are a few people who might be able to help you. Also, Google (along with the listed Data Science forums) is your friend when looking for answers.

The course was really constructive. However, for the students who are really new to coding, courses where creation of git hub account or coding to push/pull data from git hub is involved, i would suggest to add more videos related to step by step coding. Also, there are some irrelevant questions involved in the weekly quiz which are not very fruitful when it comes to learning this course. I would suggest these questions to be removed. Team can contact me on my email Id if they require complete feedback for such questions.

Hence I would rate this course 4/5.

I found this course a fairly easy introduction to the tools you will need for this series of courses, however I already had a rudimentary knowledge of Linux and Bash Shells. For the computer novice this may be more daunting. The one area that is worth spending some time on as an investment for the future is git and git-hub. Understanding how these work together is not transparent. It took me a while to figure out what I had to do to push committed files to a remote site. I think suggested reading could be more specific to guide people in this area.

The course was well structured and I compare him with two other beginner courses for data science. So far, the best one. I prefer following my own pace and opt for either video or script. The audio voice was a bit monotone but otherwise ok. I liked that there was some attention to data analysis background at University level but it was pretty basic. The final project was good since I had to redo it a couple of times before I submitted and those skills are now pretty permanent I guess:) Thanks for the clear (most of the times) guidelines.

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.

O​verall a good course and got me to understand and do some things that I have never done related to data science. As a result, I would recommend it and will be continuing with the specialization. My recommendation would be to add more descriptions and explanations to make the material clearer. For example, sometimes it wasn't clear to me in which program to run a requested task, or what it means to fork. In order to to get through the assignments, I had to look certain things up on the web, whether websites or YouTube.

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.