Healthcare Data Science in R

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About Course

This course focuses on applying conditional statements for data cleaning, healthcare automation (e.g., disease categorization, patient communication, critical case flagging, remote monitoring, AI chatbots, drug recommendations). All reinforced through practical application-based assessments.

What Will You Learn?

  • A significant portion of the course will focus on the practical application of conditional statements, generate warning messages, categorize diseases, create personalized patient communications, assign medical priority, flag critical cases, and develop treatment plans within healthcare contexts.
  • Further extending these skills, learners will design remote patient monitoring systems, perform insurance risk classification, build AI chatbots, and create pharmacy drug recommendation systems using conditional logic.

Course Content

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  • Files For Hands-On Learning

MODULE 1 OF 4: FOUNDATIONAL COURSE REVISION (Click to see module content)
A foundational course in R programming for data analytics typically covers the essential skills needed to manipulate, analyze, and visualize data using R.

MODULE 2 OF 4: R MARKDOWN- ADDING BEAUTY TO YOUR CODE PRESENTATION (Click to see module content)
This series aims to equip users with the skills to leverage R Markdown for creating professional and visually appealing code presentations. Objectives include mastering file management and knitting processes to generate HTML, Word, and PDF documents. Furthermore, the series will cover techniques for enhancing the aesthetic appeal of R Markdown outputs, including image insertion, clickable links, and basic website development principles, alongside efficient handling of code execution, tables, and graphs.

MODULE 3 OF 4: DATA CLEANING & MANIPULATION (Click to see module content)

MODULE 4 OF 4: DATA SCIENCE IN HEALTHCARE (Click to see module content)
Upon completing these modules, participants will be able to apply conditional statements in R to automate critical healthcare tasks, including disease categorization, patient message personalization, and medical priority assignment. They will also master the use of conditional logic to flag critical cases, assign treatment plans, and develop advanced systems for remote patient monitoring, insurance risk classification, AI chatbots, and pharmacy drug recommendations.

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