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SAS Enterprise Miner

Kurstitel / Course title Stufe / Level Kursformat / Course Format
Applied Analytics Using SAS® Enterprise Miner™
This course covers the skills that are 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). This course is appropriate for SAS Enterprise Miner 5.3 up to 15.1.

3 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
Customer Segmentation Using SAS® Enterprise Miner™ Business Knowledge Series
No marketing 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 as well as 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, and SOM/Kohonen method. The course focuses more on practical business solutions rather than statistical rigor. Therefore, business analysts, managers, marketers, programmers, and others can benefit from this course.

3 Für Fortgeschrittene Classroom
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 Für Fortgeschrittene Classroom Live Web Classroom
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 Für Fortgeschrittene 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.

3 Für Fortgeschrittene e-Learning
Survival Data Mining Using SAS® Enterprise Miner™ Software Business Knowledge Series
This advanced course covers predictive hazard modeling for customer history data. Designed for analysts, the course uses SAS Enterprise Miner to illustrate survival data mining methods and their practical implementation.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

4 Für Experten Classroom Live Web Classroom
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 website 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. The instructor will extensively report on both his research and consulting experience in the field. References to background material such as selected papers, tutorials, and guidelines are also provided.

Note: This course was formerly titled Advanced Analytics for Customer Intelligence Using SAS.

4 Für Experten e-Learning
Fraud Detection Using Supervised, Unsupervised, and Social Network Analytics Business Knowledge Series
A typical organization loses an estimated 5 of its yearly revenue to fraud. This course will show how learning 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, healthcare fraud, telecommunications fraud, click fraud, tax evasion, counterfeit, etc. The course will provide a mix of both theoretical and technical insights, as well as practical implementation details. The instructor will also extensively report on his recent research insights about the topic. Various real-life case studies and examples will be used for further clarification.

4 Für Experten Classroom Live Web Classroom e-Learning
Credit Scoring und Rating mit der SAS® Enterprise Miner¨ Software Business Knowledge Series
Kursziel Nach dem Besuch dieses Kurses werden Sie in der Lage sein, selbstständig Scorekarten und andere Scoring-Modelle mit der SAS Enterprise Miner Software zu bauen und zu validieren. Außerdem werden Sie die Zusammenstellung der dazu notwendigen Stichproben anleiten können. Schließlich werden Sie diese Methoden im Lichte der Anforderungen des auf internen Ratings basierenden Ansatzes zur Eigenkapitalberechnung einsetzen können.

2 Basiswissen Classroom
Managing SAS® Analytical Models Using SAS® Model Manager Version 14.2
This course focuses on the following key areas: managing SAS Model Manager data sources, creating a SAS Model Manager project, importing models into SAS Model Manager, using the SAS Model Manager Query Utility, creating scoring tasks, exporting models and projects into a SAS repository, and creating and configuring version life cycles. The course also covers generating SAS Model Manager model comparison reports, publishing and deploying SAS Model Manager models, creating SAS Model Manager production model monitoring reports, and creating user-defined reports.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual Lab time to practice.

3 Für Fortgeschrittene Classroom Live Web Classroom
SAS® Enterprise Miner™ High-Performance Data Mining Nodes
This course highlights the similarities and differences between the High-Performance nodes in SAS Enterprise Miner 14.2 and the classical nodes. A software demonstration is included.

3 Für Fortgeschrittene Classroom Live Web Classroom
SAS® Enterprise Miner™ Integration with Open Source Languages
This course introduces the basics for integrating R programming and Python scripts into SAS and SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data exploration, model prototyping, and supervised and unsupervised learning techniques.

3 Für Fortgeschrittene Classroom Live Web Classroom
Extending SAS® Enterprise Miner™ with User-Written Nodes
This course teaches you how to extend the functionality of SAS Enterprise Miner in SAS 9.

3 Für Fortgeschrittene Classroom Live Web Classroom
Entwicklung von Credit Scoring Applications mit dem SAS® Enterprise Miner™
Dieser Kurs vermittelt den Teilnehmern, wie sie eine Kredit-Scorecard aufbauen können, von Anfang bis Ende unter Verwendung des SAS Enterprise Miner 14.2 und auf Basis der von führenden Kredit- und Finanzexperten empfohlenen Vorgehensweise.

3 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
Experimentation in Data Science
This course explores the essentials of experimentation in data science, why experiments are central to any data science efforts, and how to design efficient and effective experiments.

The e-learning format of this course includes Virtual Lab time to practice.

3 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
Data Mining Techniques: Predictive Analytics on Big Data
This course introduces applications and techniques for assaying and modeling large data. The course also presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models, and mixture distribution models. Students perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics, and SAS In-Memory Statistics.

3 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
Using SAS® to Put Open Source Models into Production
This course introduces the basics for integrating R programming and Python scripts into SAS Enterprise Miner. Topics are presented in the context of data mining, which includes data exploration, model prototyping, and supervised and unsupervised learning techniques.

3 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
Predictive Modeling Using SAS® High-Performance Analytics Procedures
SAS high-performance procedures provide predictive modeling tools that have been specially developed to take advantage of parallel processing in both multithreaded single-machine mode and distributed multiple-machine mode to solve big data problems. This course gives overview of all SAS High-Performance solutions and specifically introduces the functionality in the SAS High-Performance Statistics and Data Mining procedures for predictive modeling. The course shows examples of applying advanced statistics to huge volumes of data and quickly retrain many predictive modes using all available processing power in a single-machine mode and in distributed mode.

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

4 Für Experten Classroom 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.

The e-learning format of this course includes Virtual Lab time to practice.

4 Für Experten Classroom Live Web Classroom e-Learning