Very interesting intro to data science, but the focus on command line Git may drive people away (although it was a great contribution for me!) I would use that time and effort to delve deeper in the concepts that a person must grasp in order to understand the challenges that are better tackled by using data science.

I believe there is a big and harmful disconnect between the ideas that decision makers have of their understanding of world "out there", and the real possibilities of observing and making sense of it. If this course would help narrow that gap, even a little bit, then I believe more people would be using the insights of this discipline.

Great introduction. The lessons were clear and easy to follow. Github is a new tool. I used to be shy about using Github because I didn't understand all the commands, but I'm confident I'll pick it up easily during the rest of the course. I already feel more comfortable with it.

I'm a mechanical engineer, so this is a different way of thinking for me. I was amused by the lesson on what data is and about the kinds of data analysis because the data I look at always comes a CSV file / Excel file, and I only do mechanistic analysis. So I'm looking forward to expanding my definition of data and analysis.

This course is a good first into to the topic. I think that the additional reading from the book and the Git manual will supplement it very well.

My only complain is that in the first quiz, there was a question regarding some R packages used in Machine Learning that were not covered in the slides. It took me a while to find those so I had to take the first quiz 3 times. I think this question should be revised to guide the student as to how to find these packages. Another alternative would be that in the slides there some guidance in this matter.

Otherwise, I liked to course and the final assignments.

This was a great opening course for the Data Science specialization because it talked about the tools that will be used to illustrate the concepts that are coming later. In other classes, the education on the subject matter is presented in the primary position, with tool instruction woven into those lessons. That model gives me two different priorities ( learn the topic and learn the tool ) and that competition for attention is suboptimal for me. I find myself having to revisit course content if it takes extra effort to learn some piece of technology used to demonstrate it.

Overall the coverage of github was at an appropriate level for me to understand and for that I am very grateful, as I was too lazy to force myself into learning how to use it up to this point. The coverage of installation of important programs was also a great way to introduce a subject before diving into the details. The broad coverage of the overall core curriculum was nice, and I am excited to learn how to use R as it seems like it will be around for a while (even though i'll always be faithful to my C/C++/fortran roots). I look forward to enjoying the rest of the program.

Conforme o proposto o curso é muito bom, a didática é muito boa. Mas, gostaria de deixar uma observação, em um determinado momento senti a curva de explicação muito alta. Por exemplo, no ultimo curso da semana 3 ficou muito complicado de entender, achei que entrou em alguns assuntos ainda complicado para quem está iniciando, até essa aula estava subindo numa constante mas depois parece que deu um salto muito grande, para mim, que fiquei um pouco "perdido". Fora esse ponto, gostei muito, parabéns pelo trabalho. Espero aprender tudo da maneira correta, Muito obrigado novamente.

Es un buen curso introductorio, aprendes como instalar R y RStudio en Windows y Mac, si lo haces sobre Linux (mi caso) te tocará investigar por tu cuenta. También aprendes un poco de su interfaz, paquetes que se usan y su integración con Git y GitHub (si estas en Linux también deberás investigar por tu cuenta). Al momento de mi comentario esta todo en inglés y eso no es una desventaja pero si no eres bueno con el idioma como yo tocará hacer algunos pasos extra para traducir el texto, sin embargo no es algo que impida el aprendizaje.

I love the course and the material provided. however the mechanical voice in the video is somewhat a weak point. when some real person read or present they use voice modulation as per various circumstances in the presentation, which is recognize by the listeners' brain and perceived accordingly. however, as you have mentioned the reason for use of this voice due to rapid changes, it a worth compromise. I had fun and learned a lot. Thanks to creators for their efforts and time they have put. Thanks to Coursera to bring this course.

Nice course. I got basic idea about data science, R and Rstudio. I have previous knowledge about Git. Apart from the contents, the new way of presentation is not impressing, many of the lengthy videos, it was annoying. Like, if it is a real human video, the instructor will change the tunes and it is more important in the communication. I am suggesting the same method with a human voice instead of computer generated audio/explanation. I rated 5 star for the way of progress, the context of each lesson. I felt I learned new things.

Excelente curso!! Te brinda todas las herramientas y un muy buen material de estudio, además de enseñarte minuciosamente los conceptos y partes básicas para poder aprovechar bien los primeros recursos. Y me parece un buen enfoque ya que considero que no solo se debe tratar de una "transferencia de conocimiento", sino que los alumnos debemos despertar la curiosidad y hambre por investigar para profundizar más en la diversidad de temas que tenemos por estudiar.

Muchas gracias por su dedicación y esfuerzo al elaborar el curso.

I really appreciated that the instructors took the time to go into theory and history before writing a line of code. I think an introduction or more reference to necessary statistical/mathematical knowledge would be good, but I understand if that's outside the purview of the course.

I also love the amount of extra, supplemental material you can review, whether it's a linked article or Leanpub book written by the lecturers. Would definitely recommend this course to other people interested in data.

Providing feedback means giving students an explanation of what they are doing correctly AND incorrectly. However, the focus of the feedback should be based essentially on what the students is doing right. It is most productive to a student’s learning when they are provided with an explanation and example as to what is accurate and inaccurate about their work.

Use the concept of a “feedback sandwich” to guide your feedback: Compliment, Correct, Compliment.

I think that the course is effectively introduces students to the basic toolkit of data science--informative materials, good explanations, and the accessibility to knowledge sharing through the discussion forums.

One suggestion please: It would be very convenient if you could put the links in all the videos in a place where the students could readily access them (contrary to needing to donwload the slide first to be able to access the links).

Excellent. I had a little trouble interpreting my lessons and completing the final project but I figured it out. I was under the impression from the previous videos and the assignment descriptions that I needed to do everything via "Git Bash" in my Mac Terminal, rather than just going and doing it all on Github.com. Either I completely misinterpreted or something was lost in translation right there towards the end. Loved the course though.

This course is awesome. It takes you by your hand from the very beginning and leads you through all the process to install softwares and sign in on the most up-do-date tools to work on Data Science. And, besides being very friendly, it doesn't stay superficial on the subject. Another highlight is the quality of the material and of the experienced instructors. Excellent value for the investment. Highly recommended if you are a beginner.

I think they have pretty much nailed it with this course/specialization. I have tried several courses on data science from Microsoft, EDX and Coursera and they always seems to lack something. They are either too nimble, lacking the big picture, or they are too long or badly designed. The team is great, they have a very hands on experience on data science and the learning goals are presented in a palatable manner. Excellent course!

This course provided an excellent introduction to Data Science, the tools used for analysis and basic concepts. It helped me to develop a solid foundation for future coursework in the Data Science track. The presentations are concise, giving necessary details for understanding without excess volume. Plenty of quality references are provided for those, including myself, who want to learn more about the data science discipline.

It was a great start of the specialization course. I completed in just one day. So in fact if you get a time of 4-5 hours in any holiday or free day just do this. It will create interest in you to learn more and more with quick space. I highly recommend this course to the people who has some interest in data science and want to learn more but doesn't get a clue where to start. This is actually a perfect platform to start .

Very good course to get the basics of what the overall specialisation will entail. Great information on setting up your system for any of the other courses in the specialisation. Information on Command Line programming and version control with Git and GitHub set-up proving to be useful. Good to get an introduction to the process of submitting peer reviewed assignments for future courses. Overall good introductory course.

This course is perfectly suitable for people who have passion in R or data science, and has some basic concept of statistics but not a specialist yet. I am a undergraduate student majoring in public health .I choose R for my first scientific tool because of its various utility and applicability to data analysis. But I also want to try to learn some Python in the future. Thank you professors, and my dreamy school--JHU!!

To start with, I have enjoyed this experience of online learning a lot and this is my first online course. The structure of the course is very well designed. The platform is very user-friendly. Although it was just a basic introductory course, it is just a step towards learning more interesting things from a renowned university and very good professors. Looking forward for more quality courses. One step at a time!

Comprehensive set up guide and good staging for how to truly be effective in using data to answer questions. Only feedback would be that links should be made clickable from video page and perhaps that notes should be made downloadable. I'd also suggest that when a question is answered incorrectly, recommending users reread notes instead of rewatching the videos as it's easier to skim notes than it is a video.

The teaching methodology of presentation were excellent, I was a little disappointed for not having any more applied exercises. But I believe that my expectations were high and it was necessary to focus only on the tools on this stage. I imagine that this must be the content of the next modules, which I am looking forward to doing. I thank Coursera and JHU for the opportunity granted by the scholarship.

It is a great course for begginers in Data Science. The videos are clear and easy to understand for non-english native people. I think it is overwhelming the first step with the tools: R, RStudio, Git and Git Hub, but, if you dedicate enough time to read the material and manuals, you can do it. The fourth unit was delightful. I learn a lot and I want to say thank Coursera and JHU for this opportunity.

I have had a Github account for about a year now and I have never used it. Version control is an important part of programming and data science process so being forced to use it by via assignments was a great touch. I am comfortable loading repos now and I have the tools to use Git to have it on my local machine. A great introduction to what is turning out to be a pretty exciting and beneficial class!