Healthcare Data Analytics Toolkit

MGH Institute of Health Professions via edX MicroMasters

Overview

This Healthcare Data Analytics Toolkit MicroMasters program introduces you to the transformative role of data analysis in healthcare. It's designed specifically for those at the beginning of their journey in healthcare data analytics, providing foundational knowledge and skills. Throughout this series, you'll delve into the integration of data analytics in healthcare settings, gaining hands-on experience in analyzing data to draw meaningful conclusions and apply these insights to real-world healthcare challenges.

The introductory series focuses on the basics of data analysis and its application in healthcare settings. You’ll learn the essentials of analyzing data, understanding trends, and making data-informed decisions.

Whether you are working in a hospital, leading projects in a healthcare NGO, launching a digital health startup, or investing in cutting-edge healthcare technology, our program provides the foundation for the application of data analysis to solve healthcare's biggest challenges.

Syllabus

Courses under this program:
Course 1: Introduction to Healthcare Data Analysis

In this course, you will develop a solid foundational understanding of the most common statistical methods used in health care data analysis. These common statistical methods include descriptive statistics, data distributions, sampling distribution, hypothesis tests, visualizing and summarizing data, independent and paired sample t-tests, and ANOVA.

Course 2: Linear Relationship Data in Healthcare

In this course, you will develop a working knowledge of linear relationship data in healthcare and practice using R statistical programming to analyze this data. This course covers the concepts of correlation and linear relationships, ordinary least squares (OLS) linear regression, diagnostic tests for OLS linear regression, and dummy variables.

Course 3: Regression Models in Healthcare

In this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis and practice applying them to healthcare data in the statistical programming software R. Some of the topics covered in this course include non-linear trends, interacting variables, outliers, and logistic regression.

Course 4: Advanced Topics in Healthcare Data Analysis

In this course, you will learn about some of the complex data analysis tools and techniques that you will need to derive actionable insights from healthcare data, as well as continue learning R statistical programming to effectively apply these tools and techniques. Some of the topics covered in this course include causal inference, model specification, matching, fixed and random effects, repeated measures, dealing with missing data, and bootstrapping.

Courses

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5 weeks, 2-4 hours a week, 2-4 hours a week
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In this course, you will develop a solid foundational understanding of the most common statistical methods used in health care data analysis.

This course covers some of the most common univariate and multivariate statistical methods used in healthcare data analysis. Students will also learn how to apply these methods using a statistical software package. The course covers basic data wrangling that is necessary for data analysis. It uses examples from the healthcare industry. This course focuses on the use of statistical methods although there may be some discussion of the mathematical underpinnings and relevant formulae and assumptions necessary for understanding the application of statistical methods.

This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).

The course is comprised of 5 modules that you should complete in order, as each subsequent module builds on the previous one.
- Module 1: Descriptive Statistics and Data Distributions
- Module 2: Sampling Distribution and Hypothesis Tests
- Module 3: Visualize and Summarize Data in R
- Module 4: Independent and Paired Sample t-tests
- Module 5: ANOVA
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4 weeks, 2-4 hours a week, 2-4 hours a week
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In this course, you will develop a working knowledge of linear relationship data in healthcare and practice using R statistical programming to analyze this data. You will learn about some of the most common univariate and multivariate statical methods used in healthcare data analysis and practice applying them in a statistical software package. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.

This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).

The course is comprised of 4 modules that you should complete in order, as each subsequent module builds on the previous one.
- Module 1: Introduction to Correlation and Linear Relationships
- Module 2: Simple OLS Linear Regression
- Module 3: Dummy Variables and Multiple OLS Linear Regression
- Module 4: OLS Linear Regression Diagnostic Tests
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4 weeks, 2-4 hours a week, 2-4 hours a week
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In this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.

This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).

The course is comprised of 4 modules that you should complete in order, as each subsequent module builds on the previous one.
- Module 1: Non-Linear Trends
- Module 2: Interacting Variables and Finding Outliers
- Module 3: Logistic Regression
- Module 4: Logistic Regression Variants
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6 weeks, 2-4 hours a week, 2-4 hours a week
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In this course, you will learn about some of the advanced skills you will need for real-world healthcare data analysis. You will continue to practice these skills using the statistical programming software called R and examples from the healthcare industry. The topics covered in this course will help you to engage in the more advanced data wrangling that is often necessary for data analysis and to make data-informed decisions in the healthcare field. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.

This self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).

The course is comprised of 6 modules that you should complete in order, as each subsequent module builds on the previous one.
- Module 1: Causal Inference and Tools for Model Specification
- Module 2: Matching to Reduce Model Dependence
- Module 3: Simpson's Paradox and Fixed Effects
- Module 4: Random Effects
- Module 5: Repeated Measures and Longitudinal Data
- Module 6: Missing Data and Bootstrapping