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Docker Containers for Data Science and Reproducible Research

Course Tutorial to make your work reproducible using Docker Containers
Course from Udemy
 140 students enrolled
 en
Use Docker Containers to run R Scripts in a reproducible way
Create customized R Studio in a Docker Container [portable, automated updates]
Build personal Docker Images originated from verified publishers
Save Docker Images locally or using Docker Hub online repository
Share result of your work to your colleagues
Save and document your work with Version Control
Practical use of Version Control during development process
Run containers using Shell/Bat scripts
Use Auto-builds to update Docker images
Develop R packages
Develop Shiny Application with golem framework

Get excited!

This course is designed to jump-start using Docker Containers for Data Science and Reproducible Research by reproducing several practical examples.

Course will help to setup Docker Environment on any machine equipped with Docker Engine (Mac, Windows, Linux). Course will proceed with all steps to create custom and distributed development environment [RStudio] in a container. Forget about manual update of your Development Environment! Work as usual, add or develop the research document into your Container, test it and distribute in an image! Result will be reproducible independently on the R version, perhaps after several years...

Same about running R programs in the container. We will demonstrate this capability including testing the container on completely different machines (Mac, Windows, Linux)

Summary of ideas we will cover in this course:

  1. Reproduce and share work on a different infrastructure

  2. Be able to repeat the work after several years

  3. Use R-Studio in an isolated environment

  4. Tips to personalize work with Docker including usage of Automated Builds

What is covered by this course?

This course will provide several use cases on using Docker Containers for Data Science:

  1. Preparing your computer for using Docker

  2. Working pipeline to develop docker image

  3. Building Docker image to work with R-Studio in Interactive mode

  4. Building Docker images to run R programs

  5. Using Docker network to communicate between containers

  6. Building ShinyServer in Docker container

  7. Walk-though example of developing Shiny App as an R Package and deploying in Docker Container using golem framework

More relevant materials may be added to this course in the future (e.g. continous integration and deployment, docker-compose)

Why to take this course and not other?

Added value of this course is to provide a quick overview of functionality and to provide valuable methods and templates to build on. Focus of this course is to make a learning journey as easy as possible - simply watch these videos and reuse provided code!

Just Start using Docker Containers with your Data Science tools by reproducing this course!

Docker Containers for Data Science and Reproducible Research
$ 94.99
per course
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