STLinATL

We have moved from the information age into the data analytics age. Data-driven modeling (or empirical modeling) is describing a system through curve fitting and general data analysis. There may or may not be a theoretical description of the system, and the model is based solely on measurement and observation. Curve-fitting gives a mathematical description (i.e. "the model") and can be used to make predictions. In this hands-on workshop, you will use Python to fit curves to experimental or observational data in a breadth of applications including allometry (e.g. growth of organisms) and model rockets, for example. With no previous coding required, this can be a gentle introduction to coding and its application in data science. Although a spreadsheet is useful for linear trendlines and a few other functions, it does not scale to general data-driven modeling.