An Overview of Functional Data Analysis - 
With the advancements in data collection technologies, researchers in various fields such as epidemiology, chemometrics, and environmental science face the challenges of obtaining useful information from more detailed, complex, and intricately-structured data. Since the existing methods often are not suitable for such data, new statistical methods are developed to accommodate the complicated data structures.

Some examples of these complicated data structures include data that were collected repeatedly or could be indexed by time, which makes the conventional multivariate tools inapplicable for such cases. Such efforts in recent years have successfully addressed the intricacies of analyzing such data. As a result, Functional Data Analysis (FDA) was introduced and has been consistently growing in the field of statistics and data analysis.

Functional Data Analysis (FDA) deals with the analysis and theory of data that are in the form of functions, images and shapes, or more general objects. It arises when one of the variables or units of interest in a data set can be naturally viewed as a smooth curve or function. FDA can be thought of as a statistical analysis of samples of curves.

We aim to provide an overview of FDA through learning how to represent functional data, supplementing it with summary statistics under exploratory data analysis, and looking into functional linear models.