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