Advanced Data Analytics and Applications

Catalog description

Data-driven business decisions have been fostered by an expansion in data availability, improvements in computational power, and the design of digital- and data-centric organizations. This course builds on and extends the material covered in 'Data analytics and applications.' Specifically, we will focus on advanced tools, algorithms, and technologies that are currently being used in many industries. We will discuss a variety of business applications and tools such as causal inference, experimental and observational studies, neural networks and network effects, text mining (bag of words, term frequency, and n-grams), and advanced predictive models (deep learning and ensemble methods). This course is for MSQM: Health Analytics students.