Population Health: Responsible Data Analysis
- Offered byCoursera
Population Health: Responsible Data Analysis at Coursera Overview
Duration | 21 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Population Health: Responsible Data Analysis at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level No previous statistical knowledge is needed, just basic mathematical skills.
- Approx. 21 hours to complete
- English Subtitles: English
Population Health: Responsible Data Analysis at Coursera Course details
- In most areas of health, data is being used to make important decisions. As a health population manager, you will have the opportunity to use data to answer interesting questions. In this course, we will discuss data analysis from a responsible perspective, which will help you to extract useful information from data and enlarge your knowledge about specific aspects of interest of the population.
- First, you will learn how to obtain, safely gather, clean and explore data. Then, we will discuss that because data are usually obtained from a sample of a limited number of individuals, statistical methods are needed to make claims about the whole population of interest. You will discover how statistical inference, hypothesis testing and regression techniques will help you to make the connection between samples and populations.
- A final important aspect is interpreting and reporting. How can we transform information into knowledge? How can we separate trustworthy information from noise? In the last part of the course, we will cover the critical assessment of the results, and we will discuss challenges and dangers of data analysis in the era of big data and massive amounts of information.
- In this course, we will emphasize the concepts and we will also teach you how to effectively perform your analysis using R. You do not need to install R on your computer to follow the course, you will be able to access R and all the example data sets within the Coursera environment.
- This course will become part of the to-be-developed Leiden University master program Population Health Management. If you wish to find out more about this program see the last reading of this Course!
Population Health: Responsible Data Analysis at Coursera Curriculum
Welcome to Responsible Data Analysis
Population health: Responsible Data Analysis
How to succeed in your online class?
Meet the instructors & the team
Leiden University: Facts & Figures
About this Course
Glossary
Community Guidelines
What is your learning path?
Introduction
Structured data collection
Data privacy and security
Data description
Initial data analysis
To conclude
Structured data collection in practice
Privacy and security in practice
Case Study: Descriptive Statistics
Case study: Initial data analysis in practice
Practice quiz
Practice Quiz
R exercise
R exercise: Initial data analysis
Reflect on your goals
Test your knowledge
From data to information I: statistical inference
Introduction
Statistical Inference
Fundamentals of hypothesis testing
Choosing the right statistical test
Sample size calculation
To conclude
Confidence intervals in practice
Relation between p-values and confidence intervals
Hypothesis testing in practice
Case study: Sample size calculation in practice
Practice quiz statistical interference
Practice quiz hypothesis testing
Practice quiz: Which test?
Practice quiz: Sample size
Reflect on your goals
Test your knowledge
From data to information II: regression techniques
Introduction
Simple linear regression
Multiple linear regression
Logistic regression
Cox proportional hazards regression
To conclude
Case study: Simple linear regression in practice
Dealing with binary explanatory variables
Case study: Multiple linear regression in practice
Case study: Logistic regression in practice
Case study: Cox proportional hazards regression in practice
R - exercise: Simple linear regression
R exercise: Multiple linear regression
R exercise: Logistic regression
R-exercise: Cox proportional hazards regression
Reflect on your goals
Test your knowledge
From information to knowledge
Introduction
Are most research findings false?
Interview
Data alone does not tell the whole story
Good statistical practice
To conclude
Course Conclusion
The Likelihood of Irreproducible Research
Abandoning p-values?
Understanding Simpson's paradox
Why do we need to plan ahead?
Practice quiz
Reflect on your goals
Test your knowledge
Final assessment
Population Health: Responsible Data Analysis at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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