Automate R scripts with GitHub Actions

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.

What's inside

Syllabus

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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

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

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Ensure a strong foundation in R programming before starting the course.

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