Course: Developing Data Products
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To sign up to take the course online, please visit the Johns Hopkins Data Science Specialization.
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 class students will learn a variety of core tools for creating data products in R and R Studio in specific. Students will be evaluated via quizzes and a culminating project.
The lectures will be taught over four weeks with the third week dedicated to creating R packages.
- Shiny, rCharts, manipulate, googleVis
- Presenting data analysis, slidify, R Studio presenter.
- Students creating and deploying their projects
- Creating R packages, classes and methods, yhat.
The weekly quizzes will cover the material from that week. The quizzes don't always exactly correspond to the material for that week. However, the material is always covered before the quiz is due.
There is some optional homework that can be accessed here. More information can be found in the navbar homework tab. These exercises are optional and perhaps a little more difficult than the quizzes. They also may not exactly correspond to the quiz and lecture schedules.
The Course Project is an opportunity to demonstrate the skills you have learned during the course. It is graded through peer assessment.
Details of the Course Project are available from the first day of the course session, and your work will be due BEFORE 11:30 PM UTC on the Sunday at the end of Week 3. The deadline for Course Project submission is absolutely firm, and Late Days MAY NOT be used for the Course Project.
After the submission window closes, the evaluation phase will open. During the evaluation phase, you will evaluate and grade at least four submissions from your classmates. All four evaluations are due BEFORE 11:30 PM UTC on the Sunday at the end of Week 4. If you don't complete all four evaluations by the end of the evaluation phase, your own Course Project score will be reduced by 20%.