A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
-In this overview module, we'll go over some information and resources to help you get started and succeed in the course.
Shiny, GoogleVis, and Plotly
-Now we can turn to the first substantive lessons. In this module, you'll learn how to develop basic applications and interactive graphics in shiny, compose interactive HTML graphics with GoogleVis, and prepare data visualizations with Plotly.
R Markdown and Leaflet
-During this module, we'll learn how to create R Markdown files and embed R code in an Rmd. We'll also explore Leaflet and use it to create interactive annotated maps.
-In this module, we'll dive into the world of creating R packages and practice developing an R Markdown presentation that includes a data visualization built using Plotly.
Swirl and Course Project
-Week 4 is all about the Course Project, producing a Shiny Application and reproducible pitch.
This was the 9th course in the series I completed and I found it the most fun. I also learned a tremendous amount that I have directly applied at work. The course project is much more flexible than the prior course projects and allows a student to develop a data product of their own choosing. This allows you to work on what you enjoy - I created a Linear Programming/Optimization data product. This is also a great opportunity to pull together tools you learned in the prior courses and helps integrate how to use R, how to get help, and how to "sell" a data product to your target audience.
Bill completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
This is a great course if you want to learn how to develop data products that others can use. The course materials cover presenting data products and the course project requires the student to develop a data product in R/Shiny that is hosted on the internet. This requires a fairly strong background in R and a little HTML/Linux administration wouldn't hurt but is not necessary. This is the 9th course in teh JHU Data Science Specialization for me and it's also the one I enjoyed the most.
Anonymous completed this course.
It was a great course that you will enjoy using the last trend in data products. Further you will interact with other people and you receive a positive feedback about your last task.
Brandt completed this course, spending 3 hours a week on it and found the course difficulty to be easy.
This is the final course before the capstone in the Data Science specialization from Johns Hopkins on Coursera. Overall this is one of the coolest courses in the specialization. It covers production of data products (as the name suggests), such as web-based...
This is the final course before the capstone in the Data Science specialization from Johns Hopkins on Coursera. Overall this is one of the coolest courses in the specialization. It covers production of data products (as the name suggests), such as web-based data apps using Shiny and HTML-based presentations using Slidify, among other things. These two procedures are then used for the final project, in which the student creates his/her own Shiny-based app as well as a Slidify-produced presentation introducing the app.
This may not be the most directly useful course in the specialization for most students, but it is very interesting and more fun than most of the preceding courses. I spent a fair amount of time getting my Shiny app (a simple body mass index calculator and population distribution visualizer) to work the way I wanted, and I was pretty happy with the way it turned out. I can see a lot of use for these tools in education, and there are probably myriad applications in business as well.
Overall, four stars. One of my favorite courses in the specialization, even if I will probably rarely use what I learned.