Learn from Virtual Workshops that take you through the entire Data-Science-for-Business process of solving problems with data science, using machine learning to create interactive applications, and distributing solutions within an organization.
All the best Open Source & Software as a Tool (SaaS) tools in one place, ranked by developers and companies using them. Compare and browse tech stacks from thousands of companies and software developers from around the world.
The book covers R software development for building data science tools. As the field of data science evolves, it has become clear that software development skills are essential for producing useful data science results and products. You will obtain rigorous training in the R language, including the skills for handling complex data, building R packages and developing custom data visualizations. You will learn modern software development practices to build tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers.
Shiny application (with modules) – Saving and Restoring from RDS | R-bloggers
I am working on a Shiny application which allows the user to upload data, do some analysis and processing on each variable in the data, and finally use the processed variables to build a statistical model. As there may be hundreds of variables in the data, the user may want ...
Chapter 15 Building a Shiny app to upload and visualize spatio-temporal data | Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
Well organized and easy to understand Web building tutorials with lots of examples of how to use HTML, CSS, JavaScript, SQL, PHP, Python, Bootstrap, Java and XML.