Professional Services / SAS Opleidingen

Masterclasses

Naast ons reguliere opleidingsaanbod biedt SAS masterclasses aan via de Business Knowledge Series. 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 visual analytics. Voor u de ideale mogelijkheid om u verder te ontwikkelen in een specifiek domein.

Onze experts

Professor Bart Baesens is an assistant professor at K. U. Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). Bart has done extensive research on predictive analytics, data mining, customer relationship management, Web analytics, fraud detection and credit risk management. His findings have been published in various international journals.

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. Goutam has presented numerous programs and workshops to executives, educators, and research professionals all over the world. Goutam's research has been published in many scholarly journals. He is the founder of the SAS and OSU Data Mining Certificate program as well as SAS and OSU Business Analytics Certificate program at Oklahoma State University.

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. Gordon is a practitioner, thought-leader, and teacher in the area of data mining. He has consulted for a wide range of companies.

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

Actueel aanbod
Advanced Analytics for Customer Intelligence Using SAS
Trainer: Bart Baesens, Christophe Mues
This three-day course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data-mining techniques for advanced customer intelligence applications.

Analytics: Putting it all to work
Trainer: Bart Baesens, Christophe Mues
This one-day course elaborates on how you can efficiently use and deploy both predictive and descriptive state-of-the-art analytics to optimize and streamline your strategic business processes such as marketing campaigns and/or risk management.

Credit Risk Modeling using SAS: theory and applications
Trainer: Bart Baesens, Christophe Mues
In this three-day course, students learn how to develop credit risk models in the context of the recent Basel II guidelines. The course provides a sound mix of both theoretical and technical insight, as well as practical implementation details.

Data Mining Techniques: Theory and Practice
Trainer: Gordon Linoff
In this three-day course, 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.

Fraud Detection using Supervised, Unsupervised and Social Network Analytics
Trainer: Bart Baesens, Christophe Mues
In this two-days course, 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).

Predictive Modeling for Non-Life Insurers
Trainer: Katrien Antonio
This two-day training 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 course 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.

Text Analytics and Sentiment Mining Using SAS
Trainer: Goutam Chakraborty
This two-day hands-on course 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.