The Analytics' Edge-E-F-M

Course Description:

In the big data landscape, it becomes imperative to understand what tools are available to gather and subsequently aggregate data into information and how to use this information to make better decisions. In this course, we start with descriptive analytics to summarize and visualize various data types for effective communication. We then discuss different methods for predictive analytics, i.e., the ability to learn patterns from past data to predict future events. In particular, we focus on machine learning algorithms to tackle regression and classification problems as well as unsupervised learning techniques such as clustering and dimensionality reduction. Algorithms covered in this course include penalized regression, tree-based models, gradient boosting, neural networks, PCA, k-means and hierarchical clustering.  Practical applications include fraud detection and marketing in the banking industry and revenue management at airlines and hotels. This module is a combination of lectures and coding exercises in R. No previous experience in R/other programming tools is required for this course, as all the basics are covered in the initial sessions.