SAS Enterprise Miner

Title Level Training Formats
FDP - Shaping an Advanced Analytics Curriculum
The course teaches you fundamental concepts and relevant techniques in statistical and analytical domains that are relevant in today's world. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advanced analytics for designing better curriculum and effective pedagogy.

0 No level 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 the current release.

3 Intermediate 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 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 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
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
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 Intermediate Live Web 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 Intermediate 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 Intermediate Classroom Live Web 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
Credit Risk Modeling New 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 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 Intermediate 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 Intermediate 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 Intermediate Classroom Live Web Classroom e-Learning
Advanced Analytics in a Big Data World New Business Knowledge Series

4 Advanced 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 Advanced Classroom Live Web Classroom e-Learning
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 Advanced Classroom
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 Advanced Classroom Live Web Classroom
Advanced Predictive Modeling Using SAS Enterprise Miner New
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.

The self-study e-learning includes:

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

4 Advanced Classroom Live Web Classroom e-Learning
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 Advanced Classroom Live Web Classroom e-Learning