SAS Enterprise Miner

Kurstitel / Course title Stufe / Level Kursformat / Course Format
Data Mining: Principles and Best Practices Business Knowledge Series
Data mining is an advanced science that can be difficult to do correctly. This course introduces you to the power and potential of data mining and shows you how to discover useful patterns and trends from data. Valuable practical advice, acquired during years of real-world experience, focuses on how to properly build reliable predictive models and interpret your results with confidence. Examples are drawn from several industries, including credit scoring, fraud detection, biology, investments, and cross-selling.

This course is not hands-on training for SAS Enterprise Miner software, although SAS Enterprise Miner is used by the instructor to illustrate specific modeling techniques and by students for their classroom exercises.

3 Für Fortgeschrittene 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 Classroom Live Web Classroom e-Learning
Exploratory Analysis for Large and Complex Problems Using SAS® Enterprise Miner™ Business Knowledge Series
This course is intended for analysts working with virtually any type of exploratory data analysis problem. Discovery in a complicated data set is one of the analyst's toughest problems. The course covers this discovery process using many real-world problems. There is a focus on fraud detection, with the recognition that the core principles of modeling to solve fraud detection are the basis of all exploratory data analysis. Analytical methods used in the course include decision trees, logistic regression, neural networks, link analysis, and social network analysis. In addition, analysts receive practical advice on presenting complex findings to their audience.

4 Für Experten 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.

0 Kein Level Classroom Live Web Classroom
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 Kein 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 Einsteiger 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
Weiterführende Analysen mit der SAS® Enterprise Miner™ Software
Dieser Kurs vermittelt Ihnen vertiefte Kenntnisse über die Funktionalitäten des SAS Enterprise Miner. Sie lernen weiterführende Techniken der Vorhersagemodellierung für Klassifizierung und Regression, in dem Sie einige von den neuesten Modellierungsknoten anwenden. Aktuelle Verfahren der Variablenselektion werden erläutert. Sie erlernen den Einsatz von Incremental Response-Modellierung, um die Auswirkungen einer zusätzlichen Marketing-Maßnahme auf unterschiedliche Kundengruppen beurteilen zu können. Darüber hinaus erfahren Sie, wie Sie den SAS Enterprise Miner für die Analyse von zeitlichen Daten und für Survival Data Mining einsetzen können. Tipps und Tricks für das effiziente Arbeiten mit dem SAS Enterprise Miner runden den Kurs ab.

3 Für Fortgeschrittene Classroom
Customer Life Cycle Management Using SAS® Business Knowledge Series
Marketing wisdom suggests that firms need to adopt a strategic approach toward understanding valuation of their customers and managing customer life cycle to gain competitive advantage in the marketplace. This course starts by discussing the different paths to customer profitability by linking customer loyalty with profitability and exploring the drivers of profitable customer loyalty. The course covers the popular metrics and methods used to measure customer valuation, including the concept of Customer Lifetime Value (CLV). Also discussed are the different strategies available to managers to maximize CLV over a customer's life cycle.

3 Für Fortgeschrittene Classroom
Data Mining-Techniken: Theorie und Praxis Business Knowledge Series
Dieser Kurs führt eine Data Mining-Methodik ein, die der SAS SEMMA-Methodik auf der der SAS Enterprise Miner basiert, übergeordnet ist. Der Kurs präsentiert eine Vielzahl von Data Mining-Algorithmen und vermittelt sowohl theoretische Kenntnisse als auch praktische Fähigkeiten. In diesem Kurs bearbeiten Sie alle Schritte eines Data-Mining-Projekts, beginnend mit der Problemdefinition und Datenselektion, über die Datenexploration und -transformation, das Sampling und Partitionieren von Daten bis zur Modellierung und Modellbewertung.

3 Für Fortgeschrittene Classroom
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
Best Practices in Cluster Analysis for Customer Relationship Management (CRM) Business Knowledge Series
This course focuses on best practices when doing cluster analysis, particularly when applied to CRM. However, the techniques presented are easily applicable to other analytic fields. Students with different backgrounds and skill levels will benefit from the course, which explores theory, practice, and application.

3 Für Fortgeschrittene Classroom
Text Analytics and Sentiment Mining Using SAS® Business Knowledge Series
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, e-mails, and social media. While the amount of textual data are increasing rapidly, businesses' ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. No marketing or customer intelligence program can be effective today without thoroughly understanding how to analyze textual data. Emphasizing practical skills as well as providing theoretical knowledge, this 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.

3 Für Fortgeschrittene Classroom
Big Data, Data Mining, and Machine Learning Business Knowledge Series
This course introduces the concepts of analytical computing and various data mining concepts, including predictive modeling, deep learning, and open source integration. The course introduces a wide array of topics, including the key elements of modern computing environments, an introduction to data mining algorithms, segmentation, data mining methodology, recommendation engines, text mining, and more. Throughout the course, concepts are introduced, explained, and demonstrated using approachable real-world examples. The instructor will share his extensive experience from consulting with clients on their analytic efforts as well as from his own projects throughout his career.

This course is not hands-on training for SAS Enterprise Miner software, although SAS Enterprise Miner is used by the instructor to illustrate specific modeling techniques and by students for their classroom exercises.

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
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
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
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 Basel 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 Classroom e-Learning
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 e-Learning
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
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
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
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
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.

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
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 e-Learning
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