# Modeling with R

Data mining and statistical learning methods use a variety of computational tools for understanding large, complex datasets. In some cases, the focus is on building models to predict a quantitative or qualitative output based on a collection of inputs. In others, the goal is simply to find relationships and structure from data with no specific output variable. This course takes an applied approach to understand the methodology, motivation, assumptions, strengths, and weaknesses of the most widely applicable methods in this field.

Time series data are intriguing yet complicated information to work with. While this course will provide students with a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is a small first step in being able to confidently understand what trends might exist within a set of data and the complexities of being able to use this information to make predictions or forecasts. Yet, whether it is financial, medical or weather related, this type of data is quite frequently found in much of our daily lives.