Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.
Discusses taxonomy of research designs and choosing appropriate design for implementation research, with examples of cluster-randomized trials and before/after studies.
Explores the challenges of inferring epidemiological parameters from clinical data, focusing on COVID-19 and the complexities of estimating infection fatality ratios.
Explores the challenges of observational studies, emphasizing the importance of randomization and sensitivity analysis in drawing valid conclusions from 'found data'.
Covers the Statistical Finite Element Method, focusing on the construction of a prior measure, dealing with model misspecification, and combining sensor data with FEM models.
Explores deriving bounds for causal effects using sensitivity parameters on the risk difference scale, addressing limitations and proposing new approaches.
Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.
