The aim of this course is to introduce the statistical concepts and methodologies that are often associated with making predictions with data. We begin with fundamental statistical analysis (e.g. inference, simple regression), then adds both breadth (e.g. logistic regression) and depth (e.g. model selection) to the use of regression to find the best prediction model for business forecasting. You will learn how to build predictive models with data sets in various structures (e.g. quantitative or categorical response/predictors). You will understand the trade-off between over-predicting versus under-predicting. You will practice utilizing the learned methods to solve data-based business decision problems (e.g. healthcare operations, fraud detection) through examples and case studies. R language will be used to process data and generate prediction models. No prior statistical knowledge is required, and you do not need prior knowledge about Excel or R.