New E-learning course: Profit-driven Business Analytics
The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support subsequent cost-optimal decision making.
By Bart Baesens, KU Leuven
The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support subsequent 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.
Bart Baesens, Ph. D., is a professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics and has published more than 200 scientific papers. He is the author of various books including Analytics in a Big Data World and Fraud Analytics using Descriptive, Predictive and Social Network Techniques. He also offers E-learning courses on Credit Risk Modeling and Advanced Analytics in a Big Data World. His research is summarized at www.dataminingapps.com.
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 www.data-lab.be.