SAS Enterprise Miner

Title Level Training Formats
Credit Risk Modeling Business Knowledge Series
In this course, students learn how to develop credit risk models in the context of the recent Basel II and Basel III guidelines. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details. These are illustrated by several real-life case studies and exercises.

3 Intermediate Classroom e-Learning
Applied Analytics Using SAS Enterprise Miner 5.2

3 Intermediate Classroom Live Web Classroom e-Learning
Advanced Analytics in a Big Data World Business Knowledge Series
In today's big data world, 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 and needs 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 course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data science techniques for advanced customer intelligence applications. This highly interactive course provides a sound mix of both theoretical and technical insights as well as practical implementation details and is illustrated by several real-life cases. References to background material such as selected papers, tutorials, and guidelines are also provided.

4 Expert Live Web Classroom e-Learning
Advanced Predictive Modeling Using SAS Enterprise Miner
This course covers advanced topics using SAS Enterprise Miner including how to optimize the performance of predictive models beyond the basics. The course continues the development of predictive models that begins in the Applied Analytics Using SAS Enterprise Miner 5.2 course, for example, by making use of the two-stage modeling node. In addition, some of the newest modeling nodes and latest variable selection methods are covered. Tips for working in an efficient way with SAS Enterprise Miner complete the course.

4 Expert Classroom Live Web Classroom
Decision Tree Modeling
This course covers tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees. In addition, this course discusses many of the auxiliary uses of trees such as exploratory data analysis, dimension reduction, and missing value imputation.

4 Expert Classroom Live Web Classroom
Development of Credit Scoring Applications Using SAS Enterprise Miner
This course teaches students how to build a credit scorecard, from start to finish, using SAS Enterprise Miner 7.1 and the methodology recommended by leading credit and financial experts.

3 Intermediate Classroom Live Web Classroom e-Learning
Social Network Analytics Business Knowledge Series
This course discusses how to leverage social networks for analytical purposes. Obviously, when we say "social networks," many people think of Facebook, Twitter, Google+, LinkedIn, and so on. These are all examples of networks that connect people using either friendship or professional relationships. In this course, we zoom out and provide a much more general definition of a social network. In fact, we define a social network as a network of nodes that are connected using edges. Both nodes and edges can be defined in various ways, depending on the setting. This course starts by describing the basic concepts of social networks and their applications in marketing, risk, fraud, and HR. It then defines various social metrics and illustrates how they can be used for community mining. The course also discusses how social networks can be used for predictive analytics. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details, and is illustrated by several real-life cases. The instructor extensively reports on both his research and consulting experience in the field. References to background material such as selected papers, tutorials, and guidelines are also provided.

0 No level e-Learning
Rapid Predictive Modeling for Business Analysts
This course is an introduction to SAS Rapid Predictive Modeler, a component of SAS Enterprise Miner. It provides an overview of the product and provides details on using SAS Rapid Predictive Modeler as part of the predictive modeling process. This course enables you to learn the software by practicing in an interactive simulated SAS environment.

1 Beginner e-Learning
Strategies and Concepts for Data Scientists and Business Analysts Business Knowledge Series
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.

This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience.

3 Intermediate Live Web Classroom
Neural Network Modeling
This course helps you understand and apply two popular artificial neural network algorithms: multi-layer perceptrons and radial basis functions. Both the theoretical and practical issues of fitting neural networks are covered. Specifically, this course teaches you how to choose an appropriate neural network architecture, how to determine the relevant training method, how to implement neural network models in a distributed computing environment, and how to construct custom neural networks using the NEURAL procedure.

4 Expert Live Web Classroom
Fraud Detection Using Descriptive, Predictive, and Social Network Analytics Business Knowledge Series
A typical organization loses an estimated 5 of its yearly revenue to fraud. This course shows how learning fraud patterns from historical data can be used to fight fraud. The course discusses 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, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and counterfeiting. The course provides a mix of both theoretical and technical insights, as well as practical implementation details. During the course, the instructor reports extensively on his recent research insights about the topic. Various real-life case studies and examples are presented for further clarification.

4 Expert e-Learning