Business Analytics and Big Data

Business analytics is defined as an interdisciplinary field of research that lies at the interface of computer science, operations research and data science. The aim of business analytics is to use descriptive, predictive and prescriptive models on big data to increase business performance. Hence, business analytics can give companies a strategic and operational advantage in a big data world.

This course will focus on the different parts of the business analytics process: preprocessing, analytical modelling and post-processing. Special emphasis will be given to specific business applications like customer churn prediction, credit scoring, and fraud detection. After an introduction to the different types of business analytics and its application, the most important big data preprocessing steps and analytical models are explained. Preprocessing will dig deeper into identification, cleaning, selecting and preparing of big data. Next, the most important analytical models are discussed: descriptive and predictive analytics. In descriptive analytics, unsupervised learning methods like association rule mining, dimensionality reduction and clustering are essential methods to understand and describe your data. In predictive analytics, the most important supervised learning models (e.g., linear and logistic regression, decision trees and neural networks) are introduced and their use in specific business application.

Given the rising importance of unstructured data, the course concludes with an introduction to text analytics and natural language processing, again with a special emphasis on how these methods are used in a business context.