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Advanced Topics in Healthcare Data Analysis

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

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What's inside

Learning objectives

By the end of this course, you will be able to:
Apply causal estimation using randomized controlled trials and difference-in-difference methods.
Use matching 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 and small sample sizes in datasets used for regression models.
Communicate the results of your analysis to others in simple language.

By the end of this course, you will be able to:
Apply causal estimation using randomized controlled trials and difference-in-difference methods.
Use matching 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 and small sample sizes in datasets used for regression models.
Communicate the results of your analysis to others in simple language.

Syllabus

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Makes use of advanced statistical methods from the healthcare industry in real-world applications

Provides practical examples to illustrate the application of statistical methods

Covers topics such as causal inference, matching, fixed effects, random effects, repeated measures, and missing data

May require prior knowledge of statistical programming software R

Data analysis skills may not be beginner-friendly

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Reviews summary

Hands-on healthcare data analysis with r

According to learners, this course provides a highly practical and valuable deep dive into advanced healthcare data analysis. Students particularly appreciate the extensive use of R programming for real-world healthcare datasets, finding the hands-on activities and clear explanations of complex statistical methods like causal inference and multi-level models to be immensely beneficial. Many highlight its direct applicability to their careers. However, a consistent warning is the need for a solid prerequisite understanding of R and basic statistics, as the course is fast-paced and conceptually dense. It is challenging but ultimately very rewarding for those with the right foundational knowledge.

Instructor clearly explains complex topics.

"The instructor explained complex topics clearly, making advanced concepts accessible."

"I found the concepts were broken down into understandable chunks, which really helped my learning."

"Despite the complexity of the subjects, the explanations provided were generally very easy to follow."

Covers complex statistical topics comprehensively.

"The content on causal inference and multi-level models was particularly insightful and well-explained."

"It covers essential advanced topics often overlooked in other courses, truly making it 'advanced'."

"The modules on missing data and bootstrapping were excellent and filled significant gaps in my knowledge."

Focuses on real-world use of R in healthcare.

"The practical application of statistical methods to real-world healthcare datasets using R was invaluable."

"This course provided practical methods that are immediately applicable to my work and research."

"I gained hands-on experience with R using real healthcare data sets, which is exactly what I needed."

The course is fast-paced and information-dense.

"The pacing was quite fast, I needed to re-watch lectures multiple times to fully grasp the concepts."

"It's very dense with information; be prepared to dedicate significant time and effort."

"While comprehensive, the speed at which new topics are introduced can be challenging."

Needs strong R and statistics background.

"This course definitely requires a solid foundation in R programming and basic statistics; it's not for beginners."

"I struggled initially due to my limited R background, so make sure you're comfortable with R before starting."

"Be prepared with prior knowledge of statistical concepts, otherwise, the pace might be overwhelming."

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 Advanced Topics in Healthcare Data Analysis with these activities:

Review statistical concepts and methods

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Revisit statistical concepts and methods to strengthen your foundation and ensure a better understanding of the advanced techniques covered in this course.

Browse courses on Statistical Concepts

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Review notes or textbooks on statistics
Go through online resources or articles
Complete practice questions or exercises

Review diagnostic tests and their interpretation

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Refreshes your knowledge about the various diagnostic tests used in healthcare and their interpretation, providing a stronger foundation for understanding advanced data analysis techniques.

Browse courses on Diagnostic Tests

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Review notes or textbooks on diagnostic tests
Go through online resources or articles
Complete practice questions or exercises

Join a study group or discussion forum

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Engage with peers through a study group or online discussion forum, exchanging knowledge, clarifying concepts, and fostering a supportive learning environment.

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Find or create a study group with fellow learners
Join an online discussion forum dedicated to healthcare data analysis
Actively participate in discussions, ask questions, and share insights

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Practice data manipulation and wrangling using R

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Regularly practice data manipulation and wrangling using the R programming language, strengthening your proficiency for advanced data analysis tasks.

Browse courses on Data Wrangling

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Download and install R
Go through tutorials or online courses on R basics
Find datasets online or create your own
Practice data cleaning, transformation, and visualization

Explore case studies and applications of healthcare data analysis

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Survey case studies and real-world applications of healthcare data analysis, gaining practical insights and understanding of how these techniques are used in the industry.

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Search for case studies or articles online
Attend webinars or workshops on healthcare data analysis
Review industry reports or whitepapers

Consolidate and organize course materials

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Organize and compile your notes, assignments, quizzes, exams, and any other relevant materials to enhance your review and retention of the course content.

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Create a central repository for all course materials
Organize materials into logical folders or sections
Review materials regularly

Assist or mentor fellow learners on the course platform or discussion forums

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By assisting others, you reinforce your own understanding, improve your communication skills, and contribute to a positive and collaborative learning community.

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Engage in discussion forums and answer questions from fellow learners
Provide guidance or support to learners facing difficulties
Create summaries or explanations of concepts to share with others

Develop a data analysis project using a real-world dataset

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Work on a practical project involving data analysis techniques, solidifying your understanding and giving you hands-on experience in applying these skills to real-world data.

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Identify a research question or problem to address
Gather and clean the necessary data
Analyze the data using appropriate statistical methods
Interpret the results and draw conclusions
Present your findings in a report or presentation

Career center

Learners who complete Advanced Topics in Healthcare Data Analysis will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 49 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Advanced Topics in Healthcare Data Analysis.

An Introduction to the Bootstrap

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Classic text on bootstrapping. It provides a detailed overview of the theory and practice of bootstrapping, and it includes many examples and applications.

Advanced Topics in Healthcare Data Analysis

What's inside

Learning objectives

Syllabus

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Save this course

Reviews summary

Hands-on healthcare data analysis with r

Activities

Career center

Reading list

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