Masterclasses

SAS biedt verschillende masterclasses aan. Dit zijn interactieve sessies over actuele thema’s waarbij u kunt delen in de kennis en ervaring vanuit ons wereldwijde expertnetwerk. Het thema of expertgebied staat daarbij centraal en kennis van SAS software is vaak niet nodig. U kunt hierbij denken aan thema’s als data mining, big data, credit risk en fraude. Voor u de ideale mogelijkheid om u verder te ontwikkelen in een specifiek domein.

Experts

Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on analytics and credit risk management. He is author of the books Credit Risk Management: Basic Concepts and Analytics in a Big Data World. His research is summarized at DataMiningApps. Bart also regularly tutor and provides consulting support to international firms and organizations.

Dr. Christophe Mues is an Assistant Professor at the School of Management of the University of Southampton (UK). One of his key research interests is in the business intelligence domain. Two other key research areas are knowledge discovery and data mining, with a strong interest in applying data mining techniques to financial risk management and, in particular, credit scoring.

Dr. Goutam Chakraborty is a Professor of marketing at Oklahoma State University. He has presented numerous programs and workshops to executives, educators, and research professionals all over the world. Chakraborty's research has been published in many scholarly journals, such as Journal of Interactive Marketing, Journal of Advertising Research, Journal of Advertising, Journal of Business Research, and Industrial Marketing Management. He coauthored the book Contemporary Database Marketing.

Gordon S. Linoff has had a keen interest in understanding and analyzing large data sets and in applying the results to business problems since he was a student at the Massachusetts Institute of Technology. He is a practitioner, thought leader, and teacher in the area of data mining. He has consulted for a wide range of companies. Furthermore he is author/co-author of various books, such as Data Mining Techniques for Marketing, Sales, and Customer Support.

Dr. Katrien Antonio is an Assistant Professor in actuarial science at both the University of Leuven (KU Leuven) and the University of Amsterdam. Antonio holds a PhD from KU Leuven. She is teaching non-life and life insurance mathematics. Her research puts focus on actuarial statistics, including claims reserving, ratemaking and stochastic mortality models.

Wouter Verbeke, PhD, is an Assistant Professor in business informatics at the University of Brussels (Belgium). His research is situated in the field of predictive analytics for business applications, such as customer relationship management and credit risk modeling. He teaches several courses on information systems and advanced modeling for decision making to business students, and regularly tutors courses on credit risk modeling and customer analytics to business professionals.

Aanbod

Advanced Analytics for Customer Intelligence Using SAS
Many companies have gathered huge amounts of customer data about marketing success, use of financial services, online usage and even fraud behavior. Given recent trends, it becomes increasingly important to extract, understand and exploit analytical patterns of customer behavior and strategic intelligence. This three-day master class helps clarify how to successfully adopt recently proposed state-of-the art analytical and data mining techniques for advanced customer intelligence applications.
Experts: Bart Baesens, Christophe Mues or Wouter Verbeke

Analytics: Putting It All to Work
This class starts by giving an overview of the steps involved, when working out an analytics project in a practical business setting. After discussing the key data preprocessing activities, this one-day master class elaborates on how you can efficiently use and deploy both predictive and descriptive state-of-the-art analytics to optimize and streamline strategic business processes such as marketing campaigns and/or risk management.
Experts: Bart Baesens, Christophe Mues or Wouter Verbeke

Credit Risk Modeling Using SAS
In this three-day master class, students learn how to develop credit risk models in the context of the recent Basel II and Basel III guidelines. The master class provides a sound mix of both theoretical and technical insight, as well as practical implementation details.
Experts: Bart Baesens, Christophe Mues or Wouter Verbeke

Data Mining Techniques: Theory and Practice
This three-day master class introduces a wide range of data mining algorithms and both theoretical knowledge and practical skills. You work through all the steps of a data mining project, beginning with problem definition and data selection, and continuing through data exploration, data transformation, sampling, portioning, modeling, and assessment.
Expert: Gordon Linoff

Fraud Detection Using Supervised, Unsupervised and Social Network Analytics
It is estimated that a typical organization loses about 5 percent of its revenues due to fraud each year. In this two-day master class, we will discuss how analytics can be used to fight fraud by learning fraud patterns from historical data. We will hereby discuss the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled data set) and social network learning (using a networked data set).
Experts: Bart Baesens, Christophe Mues or Wouter Verbeke

Predictive Modeling for Non-Life Insurers
This two-day master class will explain and demonstrate state of the art techniques for ratemaking and reserving in non-life insurance, as well as recently proposed advanced techniques for predictive modeling in these fields (e.g., reserving with micro-level data). The master class aims at providing a sound mix of both theoretical and technical insights, as well as practical implementation details. Concepts are illustrated with several real-life cases and exercises.
Expert: Katrien Antonio in cooperation with Triple A - Risk Finance

Text Analytics and Sentiment Mining Using SAS
Big data: It's unstructured, it's coming at you fast, and there's a lot of it. In fact, the majority of big data is unstructured and text oriented, thanks to the proliferation of online sources such as blogs, emails and social media. This two-day hands-on master class takes a comprehensive look at how to organize, manage, and mine textual data for extracting insightful information from large collections of documents and using such information for improving business operations and performance.
Expert: Goutam Chakraborty