Important Update

SAS is monitoring the Coronavirus (COVID-19) situation carefully and taking proactive measures to ensure the welfare of our learners and employees. Virtual Live Web classes, with live instructors, have been added for most public classroom events. Self-paced e-Learning is also available.

SAS Enterprise Miner

Title Level Training Formats
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 Intermediate Live Web Classroom e-Learning
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 Intermediate 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 Expert 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.

3 Intermediate e-Learning
SAS Enterprise Miner High-Performance Data Mining Nodes
This course highlights the similarities and differences between the High-Performance nodes in SAS Enterprise Miner 13.1 and the classical nodes. A software demonstration is included.

3 Intermediate Classroom
SAS Certified Predictive Modeler Exam New
In today's global economy businesses depend on advanced analytics to give them a competitive advantage. The development and implementation of models to predict targeted outcomes is quickly becoming a staple of today's most successful companies, causing this performance-based credential to be one of the fastest growing certifications available from SAS. For more details about exam topics, preparation and logistics click here.

4 Expert Classroom
SAS Certification Practice Exam: Predictive Modeling Using SAS Enterprise Miner
This practice exam is now obsolete and will be retired Dec 31, 2017. Refer to the certification web site for a replacement practice exam.

This practice exam is constructed to test similar knowledge and skills as the Predictive Modeler using SAS Enterprise Miner certification exam. Both the practice exam and the certification exam use a case study format where you are asked to perform tasks in SAS Enterprise Miner and then answer questions.

Data for the practice exam case study is provided for you so that you can perform the appropriate analyses to answer the questions. You must have access to SAS Enterprise Miner 6, 7, 13, or 14 and be able to load, create, open, and analyze data in SAS Enterprise Miner while taking the practice exam.

NEW! Need software to practice? Buy 15 hours of virtual lab time with 90-day access from date of purchase.

3 Intermediate e-Learning
SAS BIA Professional Program
Go Beyond Your Limits with SAS BIA Professional Program! SAS BIA Professional Program is a 7 months weekend program that is designed for professionals who wantto develop their analytical skills while concurrently pursuing their career path.

Under this program, you will undergo hands-on training, theory & SAS certification, in the areas of: - Data Analytics - Information Management Tailored for professionals,classes are conducted on a part-time basis on alternate weekends.

3 Intermediate Classroom
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
Predictive Modeling Using SAS High-Performance Analytics Procedures
This course introduces the functionality in the SAS High-Performance Statistics and Data Mining procedures for predictive modeling. The course shows examples of working with SAS High-Performance procedures in a single-machine mode and in distributed mode.

4 Expert Classroom
Neural Network Modeling New
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 Expert Classroom Live Web 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 Intermediate Classroom Live Web Classroom e-Learning
Fraud Detection Using Descriptive, Predictive, and Social Network Analytics New 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 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 Intermediate e-Learning
Development of Credit Scoring Applications Using SAS Enterprise Miner New
This course teaches students how to build a credit scorecard from start to finish using SAS Enterprise Miner 14.2 and the methodology recommended by leading credit and financial experts.

The self-study e-learning includes:

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

3 Intermediate Classroom Live Web Classroom e-Learning
Decision Tree Modeling New
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 Expert Classroom Live Web Classroom
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 Intermediate e-Learning
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 Intermediate 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 Intermediate 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 Intermediate Classroom Live Web Classroom e-Learning
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. 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, time-series data mining, 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 Intermediate Classroom
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 Intermediate Classroom Live Web Classroom e-Learning
Advanced Business Analytics
Advanced Business Analytics is an academic course designed to be taught on the undergraduate or graduate level during a 15-week semester. The course consists of three hours of lecture per week plus a weekly lab. The course features corporate case studies and hands-on exercises to demonstrate the concepts that are presented. Advanced Business Analytics uses software that is offered at no cost through the SAS OnDemand for Academics cloud-based software-access program and Teradata University Network.

3 Intermediate Classroom
Advanced Analytics for the Modern Business Analyst Business Knowledge Series
To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. 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 required to succeed in today's highly analytical and data-driven economy. This course introduces the basics of data management, decision trees, logistic regression, segmentation, design of experiments, and forecasting.

This course combines scheduled, instructor-led Live Web sessions with independent activities, such as reading assignments and hands-on exercises, for a highly engaging learning experience. The course is delivered over a period of five weeks with an online orientation in week one and two or three Live Web sessions per week thereafter. Students communicate with classmates and the instructor during Live Web sessions and through online forums. To achieve maximum benefit from this course, students should allocate 8 to 12 hours per week to the following:

  • participating in the two or three weekly Live Web sessions with your instructor (3.5 hours each)
  • completing all weekly assignments (these can include reading assignments and hands-on exercises) before the next scheduled Live Web session (1 to 1.5 hours a week).
Upon request, this course can also be delivered as a private 5-day on-site class at your location or a SAS training center.

3 Intermediate Classroom Live Web Classroom