This repository contains python projects related to the course- Algorithms for Data Guided Business Intelligence.
Algorithmic design principles and best practices underlying data guided Business Intelligence (BI) will be taught through a set of hands-on use cases. Analytic pipelines for solving BI problems will be introduced from the end-to-end, practical guide (i.e., cookbook) perspective. These pipelines will be implemented through a series of mini-projects covering recommender systems, sentiment analytics, online advertisement, cybercrime and online fraud detection, Internet of Things analytics, social media analytics, web logs analytics, and supply chain analytics. The space of algorithms will include but will not be limited to deep learning, information fusion from dynamic heterogeneous and attributed graphs, and causal network inference. Tutorials and projects that teach students how to handle Big Data issues will utilize Apache Spark on top of lambda architectures.