Advanced Topics in Healthcare Data Analysis
This course is part of Healthcare Data Analytics Toolkit.
In this comprehensive course from MGH Institute, you'll master advanced healthcare data analysis skills essential for deriving actionable insights from complex healthcare datasets. Over six modules, you'll deepen your proficiency with R statistical programming while exploring sophisticated analytical techniques. The curriculum covers critical concepts including causal inference, model specification, matching to reduce model dependence, fixed and random effects, Simpson's Paradox, repeated measures, longitudinal data analysis, strategies for handling missing data, and bootstrapping for small sample sizes. Through a blend of written content, video lectures, hands-on activities, and assessments, you'll learn to apply these advanced statistical methods to real-world healthcare scenarios. The course emphasizes both practical application and theoretical understanding, ensuring you grasp the mathematical underpinnings, relevant formulae, and assumptions necessary for appropriate implementation. By completion, you'll be equipped to engage in complex data wrangling, conduct sophisticated analyses, make data-informed healthcare decisions, and effectively communicate your findings in simple language. This course builds upon foundational knowledge from prerequisite courses and is designed for those seeking to enhance their analytical capabilities in the healthcare domain.
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What you'll learn
Apply causal estimation using randomized controlled trials and difference-in-difference methods
Use matching techniques to balance datasets for improved regression model results
Employ multi-level regressions with fixed and random effects and interpret their results
Implement various techniques for addressing missing data in healthcare datasets
Apply bootstrapping methods to handle small sample sizes in regression models
Communicate complex analytical results in simple, accessible language
Skills you'll gain
This course includes:
PreRecorded video
Module Quizzes, Summative Assessment
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This advanced course from MGH Institute focuses on complex data analysis tools and techniques specifically tailored for healthcare applications. Students will enhance their R programming skills while learning to apply sophisticated statistical methods to derive meaningful insights from healthcare data. The curriculum is structured across six comprehensive modules, each building upon previous knowledge to develop a robust analytical toolkit. Starting with causal inference and model specification, the course progresses through matching techniques, fixed and random effects, longitudinal data analysis, and concludes with strategies for handling missing data and bootstrapping. Throughout the course, learners engage with real-world healthcare examples, participate in hands-on activities, and complete assessments that reinforce their understanding. While the course emphasizes practical application, it also provides sufficient theoretical foundation to ensure students comprehend the mathematical principles underlying these statistical methods. By the end of the course, participants will be equipped to conduct advanced data wrangling, implement sophisticated analytical approaches, and effectively communicate their findings—skills essential for making data-informed decisions in the healthcare field.
Causal Inference and Tools for Model Specification
Module 1
Matching to Reduce Model Dependence
Module 2
Simpson's Paradox and Fixed Effects
Module 3
Random Effects
Module 4
Repeated Measures and Longitudinal Data
Module 5
Missing Data and Bootstrapping
Module 6
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Healthcare Data Analytics Toolkit
Payment options
Financial Aid
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