Course content was helpful. Some confusion in assignment questions not aligning with what was covered in lectures where it would have helped to clarify that was intentional.

A disappointing end to the pre-capstone lectures, taking the foot off the machine learning gas from course 8 with a detour back to tools and yet another Rmarkdown lecture. This basically covers building shiny apps (needed for the capstone), leaflet (maps), making presentations in RStudio - then gets lost in R Packages and Swirlify which are not very useful here. Some of this is needed in the capstone, but this course can be compressed and combined with earlier courses and make room here for something more substantial at this late stage in the specialization.

very helpful and teaching. learning practical tools for producting data products. examples in the course are not very complex, but give a very good intro for several tools.

Would like to use the time to learn more machine learning/predictive technique, etc.

Too much on things that seem unnecessary, and too little on things that are needed. Also, this course is OLD now. They really should update it, do some more on plotly, but also ad dashboards with flesdashboard.

The new platform is very versatile and easy to navigate. The page layout is much more clear. It is easy to navigate from course material to discussion boards.

I like the Quiz format, including expanding the number of choices for the multiple choice selections, but the grading was confusing. For Quiz 3, some questions came back with multiple "Well Done" comments, even when I had not selected the answer for which I was being praised. I also was told I made errors on the same question.... and this was after I answered the question (Question 2, on R generic functions) the exact same as I had answered it when I took the course earlier this year.

I was not a fan of not having to take a picture to submit work, so I am pleased that is no longer a requirement. I hope the typing pattern match is sufficient to affirm identity.

I have one comment on content specific to this class. Week 3 content lacks relevancy to the project and data products in general. I agree that knowledge of R packages, classes, and methods is an important part of understanding R. I am not sure where that fits in the Data Science curriculum as a whole, though. Maybe expanding the curriculum to include a second, more advanced R class, with a project to write our own methods, build an R package, or do something with yhat. That would assign relevant work to reinforce the lectures.

I would be happy to do further beta testing.

DCP

This is my comment as a beta tester:

1) The changes to the lessons have changed the course for the better.

2) If you want to be factually right than the statement that Bootstrap is a style should be changed on a few places. Bootstrap itself is not a style, although it is used as a style guide for the development of products. Obviously this is not a big issue and people that delve into will find the facts easily.

Bootstrap is an HTML, CSS, and JS front-end framework with a strong support for themes which people also call styles. Many themes/styles are available to build responsive, mobile-first web sites. Bootstrap was created by a designer and a developer at Twitter in mid-2010 and was released to the public in August 2011. It has become one of the most popular front-end frameworks and open source projects in the world. Bootstrap has a few easy ways to quickly get started, each one appealing to a different skill level and use case.

This is an excellent course. It's not as hard as the last three in the sequence but there is plenty to experiment with, and I was very pleased to see that we learned how to build packages, methods, and classes along the way, created an app, and even delved into building our own Swirl tutorials. While not strictly part of creating a data product, those are great things to have on the resume. I was pleased to see the capabilities of Plotly and will certainly use that. As with all of these courses, you must pay close attention to the marking rubric to get full marks. Onward to the Capstone!

After several back to back dense, high paced, steep learning courses in the specialization, this course is a welcome break. Its light, interactive and has a certain calmness about it. It touches several topics like shiny, manipulate, googlevis and plotly. As someone who has taken all courses in the specialization, I always wondered, how do I show my analysis to someone in an enterprise production environment and not as offline pdfs generated from rmd files. This course attempts to answer that question.

Excellent, relevant, and updated content and guidance through videos and assignments. If you work hard and use material from previous courses in the specialization you can start to feel how you are getting somewhere. With the technology we learned in this course I feel I can now provide usable products that provide interactivity and promote better understanding of complex data sets.

Excellent course (like the previous 8 in the specialization) and very useful for anyone working with data and involved in data storytelling. Brian (the teacher) does an awesome job explaining the concepts and how the functions and scripts in R work and interact with each other to bring about shiny apps and other visualizations. A big "Thank you!" to everyone who created this course!

My name is Jose Antonio from Brazil. I am looking for a new Data Scientist career (https://www.linkedin.com/in/joseantonio11)

I did this course to get new knowledge about Big Data and better understand the technology and your practical applications.

The course was excellent and the classes well taught by teachers.

Congratulations to Coursera team and Instructors.

Regards.

This course is cooler than the title sounds. The emphasis is on developing data apps with Shiny. In my case, I had only part of a weekend to work hard on the course project, yet I was able to make a nifty little data app that even impressed a potential employer. Leave plenty of time for brainstorming ideas for the course project and you'll find it very rewarding too.

Very good. Could go deeper in some areas but generally a good introduction to Rmarkdown, knitr, shiny and similar system and provides informtion of where to get further information where needed. The coursework was generally good but could be more demanding. considering the limited time scale this seems to be about right anyways.

Very practice oriented. After completing the Data Science Specialization courses with the course of Developing Data Products, I finally understand how important and useful R Programming is as a tool for research, data managing and inference making and for communicating results. Excellent way to crown the specialization's courses.

This is the ninth course of a series of nine courses. The creation of the apps and the didactics is very good, I just needed to do the first course of the series to get to work better the fundamentals, since this course is a bit advanced.

I loved the experience and everything I learned, I would say it is well worth it!

The material is great; and learning to use Shiny and creating an application is a lot of fun.

The only complaint I have with this course was it being put into the new Coursera platform. I felt like I was beta testing the new platform and that distracted from focusing on the course and the assignments within it.

I think GoogleVis, Plotly & LeafLet are something that you should learn already on the first courses of this specialization. Not really sure why making presentations in R would make sense given that we have Powerpoint, Keynote and Canva available to create stunning presentations for our data products.

For me the course was really interesting. Yes, all topics are not covered in deep details - but lecturers show what technologies exist and for what purposes. So if you're interested in some of them - it is up to you to discover more (useful links are included). Really nice and useful overview.

This is a very useful course in the Data Science Specialization that teaches us how to present the results of our data analysis using Shiny, Slidify and other R based data presentation tools. It also introduces open source charting APIs that we could use in our data analysis applications.

Really enjoyed this course. But you can only get as much as you put into it. A lot of students end up doing pretty mediocre apps that contain just enough to pass but never get creative. If you want to get as much as you can, then put some effort and create a nice app/presentations.

Really enjoyed learning how to build a Shiny App, and see a lot of use for this in my work environment. The Slidify product was not as useful - after 3 months of working on the project, I moved from Slidify to RPres, and was able to complete the project in very little time.

The course introduced some of the cool features in Shiny App. and other plot packages in R. Skills obtained can be used to showcase your analysis result, conduct more in-depth data exploration, and potentially used to build writing/analysis samples in job application.

This is probably one of the most interesting course in the Specialization, and it's worth the many hours spend on it. Save the videos on your YouTube, and research the Shiny web tutorial as well as the R website tutorials and Plotly.

Great fun building the apps.

I learn a lot of building data products and presentation from this course, which later can be used on my analysis or academic work. Although there might be another better tools available out there, at least I can start from what I have learn from this course.