Analytics: Putting It All to Work
Many companies are flooded with huge amounts of data available in corporate databases and/or data warehouses. A key challenge is on how to optimally manage this data overload and use analytics to better understand, manage, and strategically exploit the complex dynamics of customer behavior. This 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. Examples of business applications that are covered include credit scoring and risk modeling, customer retention and response modeling, market-basket analysis and cross-selling, customer lifetime value modeling, and Web intelligence and social network analytics.

Instructor: Dr. Thomas Verbraken, CFA, KU Leuven
September, 21 2015, Heidelberg, € 830

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Fraud Detection Using Supervised, Unsupervised, and Social Network Analytics
A typical organization loses an estimated 5% of its yearly revenue to fraud. This course will show in various real-life cases how learning about fraud patterns from historical data can be used to fight fraud. To be discussed is 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). The techniques can be applied across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, health-care fraud, telecommunications fraud, click fraud, tax evasion, counterfeiting, etc.

Instructor: Véronique Van Vlasselaer, PhD Researcher, KU Leuven
October, 12-13 2015, Heidelberg, € 1660
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Customer Segmentation Using SAS Enterprise Miner
No marketing or customer contact strategy can be effective without segmentation. While the concept of segmentation is deceptively simple, in practice it is extremely difficult to execute. Emphasizing practical skills and providing theoretical knowledge, this hands-on, comprehensive course covers segmentation analysis in the context of business data mining. Topics include the theory and concepts of segmentation, as well as the main analytic tools for segmentation: hierarchical clustering, k-means clustering, normal mixtures, RFM cell method, text-based clustering, time-series clustering, and SOM/Kohonen method.

Dr. Goutam Chakraborty, Professor of Marketing, Oklahoma State University
October, 05-07 2015, Heidelberg, € 2490
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Applied Analytics Using SAS Enterprise Miner
This course covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). In this course you learn how to define a SAS Enterprise Miner project and explore data graphically, modify data for better analysis results, build and understand predictive models such as decision trees and regression models, and compare and explain complex models. You generate and use score code and apply association and sequence discovery to transaction data.

Prof. Dr. Christina Andersson, Frankfurt University of Applied Sciences
September, 30-October, 02 2015, Heidelberg, € 2070
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Advanced Analytical Methods Using SAS Enterprise Miner Software
This course covers advanced applications of SAS Enterprise Miner. Using some of the newest modeling nodes, you learn how to use advanced prediction techniques for classification and regression. Current variable selection methods are illustrated. You learn how to apply incremental response modeling in order to evaluate the impact of marketing actions on different customer groups. Furthermore, you will gain experience in using SAS Enterprise Miner for survival data mining.

Prof. Dr. Christina Andersson, Frankfurt University of Applied Sciences
September, 14-16 2015, Vienna;
September 23-25 2015, Heidelberg, € 2070
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Text Mining for Data Scientists and Business Analysts
To be effective in a competitive business environment, analytics professionals need to use all the information available. That means not only structured information is valuable, but it is important to analyze unstructured textual information too. This can be information which is conveyed e.g. via e-mail contact with the customer directly, during complaint management, or in open-ended questions in a survey. In this course, you gain the skills data scientists and business analysts must have to successfully integrate unstructured text information into your decision-making. Discover a process model for how to handle textual information, get to know the differences between unstructured text information and structured data, learn how to prepare or parse textual data and how to use such quantified texts to find hidden nuggets which can improve your decision- making. And discover applications of text mining for pattern recognition and prediction.

Prof. Dr. Andreas Hilbert, Full Professor and Chair of Business Information Systems, Technische Universität Dresden
September, 24-25 2015, Heidelberg, € 1660
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Building Analytic Data Marts
This course teaches you how to build powerful data marts for analytical modeling in an efficient way. You learn about the ecosystem for analytic data preparation and the most commonly used analytic data structures as well as their adequacy for certain analytic business questions. You receive guidelines for how to approach the creation of important derived variables to increase the predictive power of your models. You learn tips and tricks for efficient SAS programming for the creation of analytic data marts.

Dr. Gerhard Svolba, Principal Solutions Architect, SAS Austria
October, 13-15 2015, Heidelberg, € 2070
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Strategies and Concepts for Data Scientists and Business Analysts
To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends. In this course, you gain the skills data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data.

Dr. Torsten Scholz, Analytics Trainer SAS Germany
September, 28-30 2015, Heidelberg, € 2070
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SAS Visual Analytics and Visual Statistics - Essentials
This course is designed for attendees who want to get an introduction to the exploring and reporting capabilities of SAS Visual Analytics as well as the potential of SAS Visual Statistics. The course provides a brief overview of the SAS Visual Analytics solution and then focuses on SAS Visual Analytics Explorer, SAS Visual Analytics Designer, and SAS Mobile BI.
Moreover, attendees get an introduction to SAS Visual Statistics for building predictive models in an interactive, exploratory way. The course is hands-on and it provides direct access to an environment for exercises and practical exposure.

Thomas Wende, Senior Technical Training Consultant, SAS Germany
Dr. Torsten Scholz, Analytics Trainer SAS Germany
October, 07-09 2015, Heidelberg, € 2070
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Forecasting Using SAS Software: A Programming Approach
This course teaches analysts how to use SAS/ETS software to diagnose systematic variation in data collected over time, create forecast models to capture the systematic variation, evaluate a given forecast model for goodness of fit and accuracy, and forecast future values using the model. Topics include Box-Jenkins ARIMA models, dynamic regression models, and exponential smoothing models.

Mihai Paunescu, Senior Analytics Consultant, SAS Austria
September, 28-30 2015, Heidelberg, € 2070
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Design of Experiments for Direct Marketing
This course teaches how to design marketing campaigns answering more than just one question (multi-factor-designs) and how to maximize the information that is gleaned from a marketing campaign. Interactions (or moderating variables) can also be identified. In this course, business analysts and market researchers learn how to build efficient experimental designs that generate as much information as possible for minimum cost, they test as many factors as possible in a given campaign and they apply well-known experimental design practices to direct marketing efforts. The appropriate sample size for your tests will be determined and challenges associated with analyzing experimental designs identified.

Sebastian Hoffmeister, Senior Statistical Consultant and Analytics Trainer, STATCON
September, 29-30 2015, Heidelberg, € 1660
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Self-paced E-Learning:

Advanced Analytics in a Big Data World
Given recent trends and needs in analyzing big amounts of data such as mass customization,
personalization, Web 2.0, one-to-one marketing, risk management, and fraud detection, it becomes increasingly important to extract, understand, and exploit analytical patterns of customer behavior and strategic intelligence. This e-course helps clarify how to successfully adopt recently proposed stateof- the art analytical and data science techniques for advanced customer intelligence applications.

Please register for this e-course in your home country; Webcode: BDMC13
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No other discounts apply.

The prices mentioned above do not include VAT (19%).




SAS Training Center Heidelberg
In der Neckarhelle 162
D-69118 Heidelberg
Germany Download travel information (PDF)

SAS Office Vienna
Mariahilfer Straße 116
A-1070 Wien


More information needed?

Tel: +40 6221 415 300

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