Applied mathematics research involves problems that rely on samples of data taken from the outside world. This can be seen in fields ranging from biology, physics, engineering, environmental science, and more. To understand and predict information about data that has yet to be collected, we rely on simulations of real world scenarios. We can use the collected data to compute known parameters. The relevant parameters can be used to develop models that simulate the behavior of the collected data. To predict unknown parameters and future outcomes of our scenarios, we rely on inference methods. In this short course, we will learn how to build a mathematical model from data, simulate data that closely represents the collected data, and use the simulations to make model predictions. Overall, we want the data to influence our model development decisions and for the information we learn from the models to help us understand the data.
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