New E-learning Course by Prof. Dr. Bart Baesens: Profit-Driven Business Analytics!

posted Jan 10, 2018, 2:53 AM by Yanchang Zhao   [ updated Jan 10, 2018, 2:56 AM ]

The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support cost-optimal decision making. They are advanced in that they are tailored for use in a business setting, where it is crucial to account for the costs and benefits that are related to decision making based on the output of analytical models. We call such approaches profit-driven analytics and they extend and reinforce the abilities of traditional analytics. The profit-driven perspective towards analytics that is advanced in this course contrasts with a traditional statistical perspective, which ignores the costs and benefits related to decision making based on analytical models.

In the course, we discuss both profit-driven descriptive and predictive analytics, and as well introduce uplift modeling as a stepping stone toward developing prescriptive analytical models. We also discuss a range of profit-driven evaluation measures for assessing the performance of analytical models from a business perspective. Finally, we conclude by looking into the economic impact of adopting analytics and zoom into some practical concerns related to the development, implementation and operation of analytics within an organization.

The E-learning course consists of more than 7 hours of movies, each 5 minutes on average.  Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material (movies, quizzes, scripts, ...) during 1 year. The course focusses on the concepts and modeling methodologies and not on the SAS software.  To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed.  See this link for more details.  

Course URL:

Bart Baesens

Professor Bart Baesens is a professor of Big Data & Analytics at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom).  He wrote more than 200 scientific papers on Big Data & Analytics, Machine Learning, Credit Risk Modeling and Fraud Detection and has won various awards for his research (e.g. Goodeve medal for best JORS paper, best EJOR 2013 and 2016 paper).  His findings have been published in well-known international journals and presented at international top conferences.  He is the author of 6 books Analytics in a Big Data World, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, Credit Risk Management: Basic Concepts, Credit Risk Analytics and Profit Driven Business Analytics.  He also teaches E-learning courses on Advanced Analytics in a Big Data World, Fraud Analytics, Social Network Analytics and Credit Risk Modeling.  His research is summarized at  He regularly tutors, advises and provides consulting support to international firms with respect to their  big data, analytics and credit risk management strategy. 

Wouter Verbeke

Wouter Verbeke, Ph.D., is an assistant professor at Vrije Universiteit Brussel (Belgium). His research is mainly situated in the field of predictive, prescriptive and network analytics, and is driven by real-life business problems including applications in customer relationship, credit risk, fraud, supply chain and human resources management. In 2014, he won the EURO award for best article published in the European Journal of Operational Research in the category Innovative Applications of O.R. His research is summarized at