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Automate R scripts with GitHub Actions

Deploy a model

Arimoro Olayinka Imisioluwa

Did you know you can automate R scripts to facilitate model deployment and enhance workflow efficiency in healthcare analytics?

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Did you know you can automate R scripts to facilitate model deployment and enhance workflow efficiency in healthcare analytics?

This Guided Project is designed for data scientists, healthcare analysts, and professionals in healthcare technology who want to harness the power of automation in R. In this 2-hour project-based course, you will learn how to deploy a machine learning model using R programming language and GitHub Actions, enabling seamless integration and automation of predictive tasks. To support the model deployment, you will also automate access to data in Google Sheets and send automated emails through a Google Service Account.

To achieve this, you will use a readmission model, write an R script for prediction, configure Google Sheets, Gmail, and GitHub Actions for automation, and deliver actionable insights for healthcare providers. This project is unique because it combines technical skills with real-world applications, preparing you to tackle complex challenges in data analytics.

Prior knowledge in R programming, including data manipulation (using the dplyr and tidyr packages), programming concepts (functions and control structures), GitHub repositories for code management, and basic command line use is recommended to maximize your learning experience.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Ideal for healthcare professionals who need data analysis skills to improve patient care and healthcare outcomes
Taught by Arimoro Olayinka Imisioluwa, who is an expert in healthcare analytics
A practical, hands-on approach to learning through a project-based course format
May require prior knowledge in R programming and related concepts for a smoother learning experience
Aims to address complex challenges in data analytics within the healthcare domain
Focuses on practical applications rather than theoretical concepts

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Reviews summary

R automation for model deployment

This course, Automate R scripts with GitHub Actions: Deploy a model, is designed for data scientists and healthcare analysts focusing on enhancing workflow efficiency. It aims to teach participants how to automate R scripts for machine learning model deployment using GitHub Actions. Key features include automating Google Sheets data access and sending automated emails via Google Service Account, all within the context of healthcare analytics and a real-world readmission model. The project-based course is concise, spanning 2 hours, and assumes prior knowledge in R programming and GitHub. Please note that this summary is based solely on the course description as no review data was provided.
A 2-hour project-based learning experience, highly focused.
"The 2-hour format was perfect for a quick, focused project that taught me key skills without a huge time commitment."
"I found the project-based approach highly effective for learning by doing in a short timeframe."
Requires existing knowledge in R programming and GitHub.
"Prior knowledge of R and GitHub is definitely needed; it builds on those foundations rather than teaching them from scratch."
"I found that having a solid grasp of R programming concepts really helped me maximize my learning."
Applies automation concepts to practical healthcare scenarios.
"As a healthcare analyst, this project was directly relevant to my work, using a readmission model scenario."
"The healthcare analytics context made the technical skills immediately applicable to my field."
Combines R, GitHub Actions, Google Sheets, and Gmail effectively.
"I appreciated how seamlessly it integrated R with GitHub Actions, Google Sheets, and Gmail for a complete workflow."
"The way it brought together different tools for automation was a game-changer for my projects."
Focuses on real-world automation of R scripts for model deployment.
"This course helped me see how to automate R scripts for practical model deployment. Very hands-on and applicable."
"I really valued the focus on real-world applications within healthcare analytics, making the automation concepts concrete."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Automate R scripts with GitHub Actions: Deploy a model with these activities:
Review R Programming Basics
Ensure a strong foundation in R programming before starting the course.
Browse courses on Data Structures
Show steps
  • Review basic data types, operators, and control structures.
  • Practice working with data frames and vectors.
Join a Study Group for Healthcare Analytics
Enhance your learning through peer collaboration and discussion of healthcare analytics topics.
Browse courses on Collaborative Learning
Show steps
  • Find or start a study group with peers enrolled in the course.
  • Discuss course materials, share insights, and work on assignments together.
Explore GitHub Actions for Continuous Integration
Gain deeper insights into GitHub Actions to enhance your workflow efficiency.
Browse courses on Github Actions
Show steps
  • Create a GitHub repository for the project.
  • Set up automated workflows using GitHub Actions.
  • Monitor and troubleshoot the automated workflows.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Automate R Scripts for Efficient Workflow
Enhance your R skills by practicing the automation techniques learned in the course.
Browse courses on Data Manipulation
Show steps
  • Automate data cleaning and processing tasks.
  • Create functions to automate repetitive operations.
  • Use loops and conditional statements for automated decision-making.
Deploy a Machine Learning Model Using R
Solidify your understanding of model deployment by implementing it yourself.
Browse courses on Machine Learning Model Deployment
Show steps
  • Write an R script for prediction using the prepared readmission model.
  • Configure Google Sheets to access data.
  • Set up Gmail and Google Service Account for automated emails.
  • Configure GitHub Actions for automation.
Present Automated Healthcare Analytics Findings
Strengthen your presentation skills and effectively communicate your healthcare analytics findings.
Browse courses on Data Visualization
Show steps
  • Organize and visualize the results of the deployed model.
  • Create a presentation that clearly explains the findings.
  • Practice delivering the presentation to an audience.
Contribute to Healthcare Analytics Open-Source Projects
Expand your knowledge and network by contributing to open-source projects in healthcare analytics.
Browse courses on Community Involvement
Show steps
  • Identify open-source projects relevant to healthcare analytics.
  • Contribute code, documentation, or bug fixes to selected projects.
  • Engage with the project community through discussions and forums.

Career center

Learners who complete Automate R scripts with GitHub Actions: Deploy a model will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Ideal for healthcare professionals who need data analysis skills to improve patient care and healthcare outcomes
Taught by Arimoro Olayinka Imisioluwa, who is an expert in healthcare analytics
A practical, hands-on approach to learning through a project-based course format
May require prior knowledge in R programming and related concepts for a smoother learning experience
Aims to address complex challenges in data analytics within the healthcare domain
Focuses on practical applications rather than theoretical concepts
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