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!
Welcome to the course Responsible Data Analysis! You’re joining thousands of learners currently enrolled in the course. I'm excited to have you in class and look forward to your contributions to the learning community.To begin, I recommend taking a few minutes to explore the course site. Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!
What's included
2 videos3 readings1 discussion prompt1 plugin
Show info about module content
2 videos•Total 4 minutes
Population health: Responsible Data Analysis•2 minutes
How to succeed in your online class?•2 minutes
3 readings•Total 30 minutes
Meet the instructors & the team•10 minutes
About this Course•10 minutes
Glossary•10 minutes
1 discussion prompt•Total 10 minutes
Introduce yourself•10 minutes
1 plugin•Total 2 minutes
Discover The World at Leiden University [video]•2 minutes
From Individuals to Data
Module 2•10 hours to complete
Module details
In this module, we will discuss how to obtain, store, clean and explore the data necessary to answer your research question. First, we will see how to collect data of good quality. Second, we will see how to address privacy and security when dealing with personal data. Then, we will see how to first describe and summarize your data. Finally, we will discuss the principles of initial data analysis.
What's included
6 videos4 readings6 assignments
Show info about module content
6 videos•Total 33 minutes
Introduction•2 minutes
Structured data collection•6 minutes
Data privacy and security•8 minutes
Data description•9 minutes
Initial data analysis•7 minutes
To conclude•1 minute
4 readings•Total 105 minutes
Structured data collection in practice•30 minutes
Privacy and security in practice•15 minutes
Case Study: Descriptive Statistics•40 minutes
Case study: Initial data analysis in practice•20 minutes
6 assignments•Total 445 minutes
Practice quiz•20 minutes
Practice Quiz•20 minutes
R exercise•15 minutes
R exercise: Initial data analysis•30 minutes
Reflect on your goals•180 minutes
Test your knowledge•180 minutes
From data to information I: statistical inference
Module 3•4 hours to complete
Module details
In this module, we will see how to deal with data obtained from a limited number of individuals. You will discover how statistical inference can make the connection between samples and populations. First, we will discuss important concepts such as random variation, sampling distribution and standard error. Second, we will discuss the principles of hypothesis testing. Then, we will review the moist commonly used statistical tests. Finally, we will discuss how to decide how large your study sample should be.
What's included
6 videos4 readings6 assignments
Show info about module content
6 videos•Total 37 minutes
Introduction•1 minute
Statistical Inference•9 minutes
Fundamentals of hypothesis testing•8 minutes
Choosing the right statistical test•8 minutes
Sample size calculation•8 minutes
To conclude•2 minutes
4 readings•Total 105 minutes
Confidence intervals in practice•20 minutes
Relation between p-values and confidence intervals•30 minutes
Hypothesis testing in practice•25 minutes
Case study: Sample size calculation in practice•30 minutes
6 assignments•Total 105 minutes
Practice quiz statistical interference•20 minutes
Practice quiz hypothesis testing•15 minutes
Practice quiz: Which test?•15 minutes
Practice quiz: Sample size•15 minutes
Reflect on your goals•10 minutes
Test your knowledge•30 minutes
From data to information II: regression techniques
Module 4•7 hours to complete
Module details
In this module, we will discuss the basic principles of regression modeling, a collection of powerful tools to analyze complex data. We will start simple, and increase the complexity of the models step by step. We will start with linear regression, used with continuous outcomes. Then we will continue with logistic regression, which can be used to model binary variables, and finally we will consider regression with time to event outcomes.
What's included
6 videos5 readings6 assignments
Show info about module content
6 videos•Total 36 minutes
Introduction•1 minute
Simple linear regression•8 minutes
Multiple linear regression•8 minutes
Logistic regression•7 minutes
Cox proportional hazards regression•10 minutes
To conclude•2 minutes
5 readings•Total 100 minutes
Case study: Simple linear regression in practice•30 minutes
Dealing with binary explanatory variables•0 minutes
Case study: Multiple linear regression in practice•15 minutes
Case study: Logistic regression in practice•25 minutes
Case study: Cox proportional hazards regression in practice•30 minutes
In this module , we will cover the critical assessment of data analysis results, and we will discuss challenges and dangers of data analysis in the era of big data and massive amounts of information. First, we will see how bad data analysis practice can dramatically impact scientific progress. Second, we will address the hot topic of how to report uncertainty in scientific findings. This has been object of big controversy in the scientific literature. We invited two experts to present their different points of view. Then, we will discuss different forms of bias. Finally, we will give you tips and tricks to write a perfect statistical plan.
Special about this week is that we are working with a discussion group about some difficult social situations you might encounter when doing your own research. Most of us who have worked in research might have been through those, and if you feel comfortable, please do share your thoughts about what you think is appropriate, and follow the threads as the rest of us reply!
Leiden University is one of Europe's foremost research universities. This prominent position gives our graduates a leading edge and prepares them for careers both within and outside of academia. Leiden University is the oldest university in the Netherlands, founded in 1575. Our motto is: Praesidium Libertatis (Bastion of Liberty) - Freedom of spirit, thought and expression. Leiden University has a campus in Leiden and The Hague, with 7 faculties, 47 Bachelor Programmes, 79 Master Programmes and nearly 30,000 students.
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Is financial aid available?
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