Data Analytics and Applications

Catalog description

The course investigates how to use data to impact business practices. We will study the core ideas behind data analytics, challenges associated with big data, and the interplay between data science and business decisions. We will focus on general principles that are long lasting despite the rapid changing technology and on 'hands-on' analyses of actual datasets to develop methodologies. Topics may include data mining, advanced topics in linear regression, quantile regression, classification (logistic regression, support vector machine, classification trees), model selection and overfitting, cross-validation, similarity, k-nearest neighbor, and clustering. A variety of real life examples will be discussed such as customer retention, health risk prediction, social media analysis, real-time online advertisement, and data mining contests. This course is for MSQM: Health Analytics students.