HOT

Docker Containers for Data Science and Reproducible Research

Development

Rating3.7
Enroll Now

Docker Containers for Data Science and Reproducible Research, available at $59.99, has an average rating of 3.7, with 124 lectures, 2 quizzes, based on 35 reviews, and has 293 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists willing to use Docker in their toolset or Anyone willing to deploy R script on Docker Container or Anyone willing to use R-Studio on Docker Container or Anyone curious about Docker for Data Science It is particularly useful for Data Scientists willing to use Docker in their toolset or Anyone willing to deploy R script on Docker Container or Anyone willing to use R-Studio on Docker Container or Anyone curious about Docker for Data Science.

Enroll now: Docker Containers for Data Science and Reproducible Research

Summary

Title: Docker Containers for Data Science and Reproducible Research

Price: $59.99

Average Rating: 3.7

Number of Lectures: 124

Number of Quizzes: 2

Number of Published Lectures: 120

Number of Published Quizzes: 2

Number of Curriculum Items: 126

Number of Published Curriculum Objects: 122

Original Price: $199.99

Quality Status: approved

Status: Live

What 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

Who Should Attend

  • 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

Target Audiences

  • 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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Quick Win – Run R-Studio IDE in a Docker Container

Lecture 3: Quick Win – Run R program in a Docker Container

Lecture 4: Quick Win – Run R Shiny Application in a Docker Container

Chapter 2: Install Docker, Preparations, etc

Lecture 1: Introduction to this section

Lecture 2: Create an Account for DockerHub

Lecture 3: Docker Desktop for Mac

Lecture 4: Docker Desktop Settings

Lecture 5: Docker Desktop for Windows

Lecture 6: Docker for Linux

Lecture 7: Github Desktop

Chapter 3: Build a personal Docker Image for R-Studio IDE

Lecture 1: Motivation of this section

Lecture 2: Create a Folder for our project

Lecture 3: Put things under Version Control [Git]

Lecture 4: Build the image

Lecture 5: Taking care about Documentation (update file Readme)

Lecture 6: List all images

Lecture 7: Run the container

Lecture 8: Mapping computer folders to container

Lecture 9: Update readme file

Lecture 10: Create Executable File to run Container… make it easy

Lecture 11: Save image to the Docker Hub

Lecture 12: Saving image locally

Lecture 13: Deleting the image from your Computer

Lecture 14: Restore image from the local archive file

Lecture 15: Check running container from another terminal

Lecture 16: Install R Package in running RStudio and save image

Lecture 17: Push Changes to Docker Hub

Lecture 18: Save a new version of the image using Tags

Lecture 19: Setup Automated Build of the image

Lecture 20: Verify Automated Build

Lecture 21: Add a badge to the README file [nice to have]

Lecture 22: Practical use of R-Studio in Docker Container

Lecture 23: Summary of this chapter

Chapter 4: Build a personal Docker Image with R Statistical Software

Lecture 1: Motivation of this section

Lecture 2: Let's again start with a Version control!

Lecture 3: Auto-building an image on Docker Hub

Lecture 4: Why to build own image (security)?

Lecture 5: Pull our personalized image

Lecture 6: Test our container!

Lecture 7: Summary of this chapter – ready for reproducible research

Lecture 8: Blueprint: Managing Docker Images

Lecture 9: Deleting un-used containers/images

Chapter 5: Customized image to make our work Reproducible

Lecture 1: Motivation of this section

Lecture 2: Blueprint for organizing Reproducible Research on Docker Containers

Lecture 3: Create our research document!

Lecture 4: Adding R Markdown to the Docker Image

Lecture 5: Test the container

Lecture 6: Push image (repetition)

Lecture 7: Publish our repository

Lecture 8: Share results: trying image on another machine

Chapter 6: Customized image to run R Scripts

Lecture 1: Motivation of this section

Lecture 2: Review Dockerfile

Lecture 3: Build and Push the image

Lecture 4: Test our container

Lecture 5: Publish our work in GitHub repository

Lecture 6: Summary of this section

Chapter 7: Docker Networks – publishing and consuming API using different Containers

Lecture 1: Introduction to multicontainer applications

Lecture 2: Note on Docker Compose

Lecture 3: Case Study: Application to verify hardware components

Lecture 4: Create Plumber API

Lecture 5: Add Plumber API into the image

Lecture 6: Create Docker Network

Lecture 7: Test connectivity between running containers

Lecture 8: Prepare to Test Multi Container Application

Lecture 9: Test Multi Container Application

Chapter 8: Shiny App in the Docker Container

Lecture 1: Motivation of this section

Lecture 2: Quick Win – rocker/shiny

Lecture 3: Rocker/shiny starting our Shiny Server in Docker Container

Lecture 4: Mapping: Shiny App <> Shiny Server <> Docker container

Lecture 5: Placing Shiny App into Docker Container

Lecture 6: More professional development of ShinyApps in Containers

Chapter 9: P1 Setup Project: Develop Shiny App as an R package in Docker Container

Lecture 1: Motivation of this section

Lecture 2: Create new Project

Lecture 3: Adding R package description

Lecture 4: Set Options to the package

Lecture 5: Add Version Control

Lecture 6: Building the package, finish step 1

Chapter 10: P2 golem explained: Develop Shiny App as an R package in Docker Container

Lecture 1: Investigation tactic: Let's see developed example. Step 1: Clone others work!

Lecture 2: Step2: How to run Shiny App built with Golem framework?

Lecture 3: Step 3: Reverse engineer Golem Framework!

Chapter 11: P3 Dive in Version Control: Develop ShinyApp as an R package in Docker Container

Lecture 1: Deep dive in Version Control

Lecture 2: Nothing works – what to do?

Lecture 3: Back in history in a separate branch

Lecture 4: Revert single changes: commit frequently!

Lecture 5: How to delete branches?

Chapter 12: P4 Business Logic: Develop ShinyApp as an R package in Docker Container

Instructors

Rating Distribution

  • 1 stars: 3 votes
  • 2 stars: 1 votes
  • 3 stars: 5 votes
  • 4 stars: 12 votes
  • 5 stars: 14 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

You may also like

Author School Visits 101: Children's Book Marketing

YouTube Masterclass – The Best Guide to YouTube Success

2024 Marketing For Design, Tech, Or Digital Marketing Agency