Students will be able to:
•  Appraise different types of modern analysis approaches
commonly used to study public health and biomedical data
•  Critically compare and evaluate the strengths and weaknesses
of different analytic approaches
•  Formulate the appropriate analytic approach for a given data 
set
•  Articulate a research question and outline a data analytic
approach suitable to answering this question for a given data 
set
•  Design and implement successful data analyses using a
state-of-the-art analysis software
•  Explain the importance of workflow management and
reproducibility for successful data analysis
•  Judge the usefulness of different analysis tools described in
the primary literature on epidemiology methodology 
•  Evaluate state-of-the art analysis approaches from the
research literature 
•  Critically appraise analyses presented in published studies,
identify strengths and weaknesses of other people’s analyses
•  Assess the strengths and weaknesses of different approaches 
to representing the results of data analyses
•  Summarize analysis results in a way that is easily
understandable for different audiences, such as lay persons,
decision makers, and expert colleagues

1.	Introduction to data analysis
2.	The data analysis workflow
3.	Overview of data types and analysis approaches
4.	Getting and pre-processing data
5.	Preliminary and graphical data analyses
6.	Hypothesis testing and model comparison 
7.	Quantifying uncertainty
8.	Model predictions
9.	Presentation of analysis results