The techniques presented in come from the areas of machine learning, statistics, and operations research. Machine learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data and dimensionality reduction of data sets. Operations research is concerned with the performance of an IoT system by constructing a model of the system under study and then carrying out a what-if analysis. The book also describes simulation techniques. Key Features Harry G. Perros is a Professor of Computer Science at North Carolina State University, an Alumni Distinguished Graduate Professor, and an IEEE Fellow. He has published extensively in the area of performance modeling of computer and communication systems.