Population Health: Responsible Data Analysis

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  • Welcome to Responsible Data Analysis
    • 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!
  • From Individuals to Data
    • 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.
  • From data to information I: statistical inference
    • 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.
  • From data to information II: regression techniques
    • 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.
  • From information to knowledge
    • 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!

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