Docker Containers Data Science Reproducible Research

Course

DOCKER CONTAINERS DATA SCIENCE REPRODUCIBLE RESEARCH

Category

Docker Computer Courses

Eligibility

Graduates and Technology Aspirants

Mode

Online and Offline Classes

Batches

Week Days and Week Ends

Duration :

30 to 45 days

Docker What will you learn?

•How to work with Docker Tool.
•You learn how to use Docker code.
•Learn to code with Docker the easy way.
•How to create your own Docker components from scratch.
•Learn about Docker in a step by step approach
•Become a Docker Certified Developer! Learn all Docker Developer topics
•You will learn how to draw different Docker entities through code.
•Discover how to correctly test instance identity as well as equality in Docker.
•Learn Docker Complete Course with Professionals from Scratch and Become a Pro in Docker

docker containers data science reproducible research Training Highlights

•Get job-ready for an in-demand career
•Resume & Interviews Preparation Support
•Highly competent and skilled IT instructors
•Personal attention and guidance for every student
•Facility of Lab on cloud available (based on booking)
•We provide you with your recorded session for further Reference
•Curriculum based on course outlines defined by in-demand skills in Python.
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises

Who are eligible for Docker

•.Net Developer, SilverLight, MVC3, Entity Framework 4, WCF, SQL/PLSQL, c#, SQL Server 2008, HTML5, .Net
•Java Developer, Mainframes Developer, Sap Consultant, Qa, Network Operations, C++ Developer, Wintel Admin
•Microsoft Azure, Azure, Sql Azure, Cloud Computing, Cloud Testing, SQL, Cognos Framework Manager, Query Studio, Oracle, Business Objects, Issue Resolution
•Qa Automation, Ror Developer, Android Developer, Bluetooth Developer, Android Application Developer, Embedded Development, Javascript, Ruby, Postgres, SQL
•ux, ui, Python Developers, Qa Automation, sales, Ui Development, Ux Design, Software Development, Python, Qa Testing, Automation Testing

DOCKER CONTAINERS DATA SCIENCE REPRODUCIBLE RESEARCH Topics

•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!
•Who 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
•Quick Win – Run R-Studio IDE in a Docker Container
•Quick Win – Run R program in a Docker Container
•Install Docker, Preparations, etc
•Introduction to this section
•Create an Account for DockerHub
•Docker Desktop for Mac
•Docker Desktop Settings
•Docker Desktop for Windows
•Docker for Linux
•Github Desktop
•What is the purpose of these programs?
•Build a personal Docker Image for R-Studio IDE
•Motivation of this section
•Create a Folder for our project
•Put things under Version Control [Git]
•Build the image
•Taking care about Documentation (update file Readme)
•List all images
•Run the container
•Mapping computer folders to container
•Update readme file
•Create Executable File to run Container… make it easy
•Save image to the Docker Hub
•Saving image locally
•Deleting the image from your Computer
•Restore image from the local archive file
•Check running container from another terminal
•Install R Package in running RStudio and save image
•Push Changes to Docker Hub
•Save a new version of the image using Tags
•Setup Automated Build of the image
•Verify Automated Build
•Add a badge to the README file [nice to have]
•Setup RStudio on Docker Container
•Practical use of R-Studio in Docker Container
•Summary of this chapter
•Build a personal Docker Image with R Statistical Software
•Let’s again start with a Version control!
•Auto-building an image on Docker Hub
•Why to build own image (security)?
•Pull our personalized image
•Test our container!
•Summary of this chapter – ready for reproducible research
•Blueprint: Managing Docker Images
•Deleting un-used containers/images
•Customized image to make our work Reproducible
•Blueprint for organizing Reproducible Research on Docker Containers
•Create our research document!
•Adding R Markdown to the Docker Image
•Test the container
•Push image (repetition)
•Publish our repository
•Share results: trying image on another machine
•Customized image to run R Scripts
•Review Dockerfile
•Build and Push the image
•Test our container
•Publish our work in GitHub repository
•Summary of this section
•Docker Networks – publishing and consuming API using different Containers
•Introduction to multicontainer applications
•Note on Docker Compose
•Case Study: Application to verify hardware components
•Create Plumber API
•Add Plumber API into the image
•Create Docker Network
•Test connectivity between running containers
•Prepare to Test Multi Container Application
•Test Multi Container Application
•Shiny App in the Docker Container
•Quick Win – rocker/shiny
•Rocker/shiny starting our Shiny Server in Docker Container
•Mapping: Shiny App <> Shiny Server <> Docker container
•Placing Shiny App into Docker Container
•More professional development of ShinyApps in Containers
•P1 Setup Project: Develop Shiny App as an R package in Docker Container
•Create new Project
•Adding R package •Set Options to the package
•Add Version Control
•Building the package, finish step 1
•P2 golem explained: Develop Shiny App as an R package in Docker Container
•Investigation tactic: Let’s see developed example. Step 1: Clone others work!
•Step2: How to run Shiny App built with Golem framework?
•Step 3: Reverse engineer Golem Framework!
•P3 Dive in Version Control: Develop ShinyApp as an R package in Docker Container
•Deep dive in Version Control
•Nothing works – what to do?
•Back in history in a separate branch
•Revert single changes: commit frequently!
•How to delete branches?
•P4 Business Logic: Develop ShinyApp as an R package in Docker Container
•Adding Business Logic
•Develop User Interface Part 1
•Develop User Interface Part 2
•Develop Server logic Part 1
•Develop Server logic Part 2
•P5 Make it as a Package: Develop ShinyApp as an R package in Docker Container
•Detecting errors during R package checks
•Adding function dependencies with golem framework
•Adding tests
•Adding golem recommended tests
•Debugging failed tests
•P6 Setup Continuous Integ.: Develop ShinyApp as an R package in Docker Container
•Setup Travis CI P1
•Setup Travis CI P2
•Making Pull Request and make use of CI travis tests
•P7 Deploy Image: Develop ShinyApp as an R package in Docker Container
•Checking R package with R Hub
•Create Dockerfile using golem framework
•Build docker image
•Run the container with Shiny App as an R package!
•Stop Docker Container, push to Docker Hub
•P8 CI in Action: Develop ShinyApp as an R package in Docker Container
•Setup Autobuild of Docker Image
•Summary
•Summary of the course
•Useful Materials Blogs, Best practices, etc