This course offers a comprehensive introduction to Functional Data Analysis (FDA) as a vital tool for analyzing high-dimensional datasets, often referred to as Big Data. The primary goal is to provide an accessible introduction to FDA, tailored to both theory-focused and applied-oriented audiences, ensuring effective engagement with the material. Objectives include covering fundamental FDA concepts (e.g., basis expansion, principal components, etc), functional regression models, dependent data structures (e.g., time series and spatial data), and dimensionality reduction techniques essential for extracting signals from complex datasets. Practical implementation will be demonstrated using various economic datasets, such as electricity market data, climate/environmental data, and income profiles. Additionally, the course will provide references to advanced research for students interested in further exploring specific FDA topics.
