What you would learn in Docker Containers for Data Science and Reproducible Research course?
The course is intended to get you up and running the process of using the Docker containers for data science and reproducible research by reproducing various concrete examples.
The course will guide you through the process of setting up Docker Environment on any machine that is equipped that runs Docker Engine (Mac, Windows, Linux). The course will go through the steps needed to build an environment for development that is custom and distributed [RStudio] within the form of a container. Don't bother with manual updates on Your Development Environment! As usual, you can incorporate or build the research document in your container, test it, and distribute the image! The result will be reproducible using an R version, maybe after a while...
Similar to the running of R programs inside the container. This will be demonstrated by demonstrating this feature and testing the container on a variety of devices (Mac, Windows, Linux)
The main ideas we'll be covering in this course:
Reproduce and share your work with another infrastructure
You will be able to continue the same task after several years
Utilize R-Studio in a closed environment
Tips to customize work using Docker, such as the use of Automated Builds
What is covered in this class?
This course will cover a variety of examples of how to use Docker Containers to support Data Science:
Preparing your computer for using Docker
Working pipeline to develop docker image
Making Docker images to use R-Studio using Interactive Mode
Making Docker images that run R programs
Utilizing the Docker network to connect between containers
The development of ShinyServer is done in a Docker container
An example of a walk-through of how to develop Shiny App in R Package and deploying it into a Docker Container using golem framework
Course Content:
- Utilize Docker Containers to run R Scripts in a reproducible manner
- Create a customized R Studio inside Docker Containers that can be portable and have automatic updates[portable, automated updates
- Create your own personal Docker Images sourced from trusted publishing
- Store Docker Images locally or using Docker Hub online repository
- You can share the result of your work with your colleagues
- Document and save your work using Version Control
- Use in the field of Version Control during the development process
- Run containers using Shell/Bat scripts
- Make use of Auto-builds to upgrade Docker images
- Create R-based packages
- Create a Shiny Application with a golem framework
Download Docker Containers for Data Science and Reproducible Research from below links NOW!
