Disclosure: when you buy through links on our site, we may earn an affiliate commission.

Docker Containers for Data Science and Reproducible Research

Course Tutorial to make your work reproducible using Docker Containers
4.3
4.3/5
(24 reviews)
253 students
Created by

9.1

CourseMarks Score®

9.0

Freshness

8.2

Feedback

9.4

Content

Platform: Udemy
Video: 7h 0m
Language: English
Next start: On Demand

Table of contents

Description

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:
•Reproduce and share work on a different infrastructure
•Be able to repeat the work after several years
•Use R-Studio in an isolated environment
•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:
•Preparing your computer for using Docker
•Working pipeline to develop docker image
•Building Docker image to work with R-Studio in Interactive mode
•Building Docker images to run R programs
•Using Docker network to communicate between containers
•Building ShinyServer in Docker container
•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!

You will learn

✓ 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

Requirements

• GitHub account
• Mac or Windows PC [can also be applicable for Linux]
• Basic knowledge of R programming language is preferred but not necessary
• Willing to learn and use R Statistical Software
• Basic knowledge of command line is preferred but not necessary

This course is for

• Data Scientists willing to use Docker in their toolset
• Anyone willing to deploy R script on Docker Container
• Anyone willing to use R-Studio on Docker Container
• Anyone curious about Docker for Data Science
Senior Engineering Specialist and Instructor
Hello, I am really excited that you read my little story here!
I am a Chemical Engineer by education, Problem Solver by nature and Instructor by hobby. I currently work in Swiss Multinational Company as Senior Engineering Specialist in R&D. I like to learn and apply modern technology to gain value. I believe that it is very important to always learn new technologies and apply them to reduce inefficiencies by finding complex patterns or applying new methods to close gaps.
In my public educational projects I would like to bring some ideas on how to apply computing power to be more productive. How to collect data in a smarter way using simple tools, how to analyze data to take a decision, and … why not to automate the decision using Artificial Intelligence? I will try to cover very practical side of technology, show how to benefit from it with concrete examples.
p.s. I will try my best to provide the best possible learning experience. If it would not be the case I would be very happy to receive any constructive feedback on how can I be better.
Browse all courses by on Coursemarks.
Platform: Udemy
Video: 7h 0m
Language: English
Next start: On Demand

Students are also interested in