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.
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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.
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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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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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
Show steps
-
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
Show steps
-
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
Show steps
Engage with peers through a study group or online discussion forum, exchanging knowledge, clarifying concepts, and fostering a supportive learning environment.
Show steps
-
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
Five other activities
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Show all eight activities
Practice data manipulation and wrangling using R
Show steps
Regularly practice data manipulation and wrangling using the R programming language, strengthening your proficiency for advanced data analysis tasks.
Browse courses on
Data Wrangling
Show steps
-
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
Show steps
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.
Browse courses on
Case Studies
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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
Show steps
Organize and compile your notes, assignments, quizzes, exams, and any other relevant materials to enhance your review and retention of the course content.
Show steps
-
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
Show steps
By assisting others, you reinforce your own understanding, improve your communication skills, and contribute to a positive and collaborative learning community.
Show steps
-
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
Show steps
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.
Show steps
-
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
Review statistical concepts and methods
Show steps
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
Show steps
- Review notes or textbooks on statistics
- Go through online resources or articles
- Complete practice questions or exercises
Review diagnostic tests and their interpretation
Show steps
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
Show steps
- 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
Show steps
Engage with peers through a study group or online discussion forum, exchanging knowledge, clarifying concepts, and fostering a supportive learning environment.
Show steps
- 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
Practice data manipulation and wrangling using R
Show steps
Regularly practice data manipulation and wrangling using the R programming language, strengthening your proficiency for advanced data analysis tasks.
Browse courses on
Data Wrangling
Show steps
- 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
Show steps
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.
Browse courses on
Case Studies
Show steps
- 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
Show steps
Organize and compile your notes, assignments, quizzes, exams, and any other relevant materials to enhance your review and retention of the course content.
Show steps
- 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
Show steps
By assisting others, you reinforce your own understanding, improve your communication skills, and contribute to a positive and collaborative learning community.
Show steps
- 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
Show steps
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.
Show steps
- 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
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