SAS Enterprise Miner

名称 水平 培训形式
使用SAS Enterprise Miner进行应用分析
  • 本课程适用于SAS Enterprise Miner 5.3到14.2版本。 本课程涵盖了使用SAS Enterprise Miner 丰富工具集组装分析流程图所需的技能,用于模式发现(分割、关联和序列分析)和预测建模(决策树、回归和神经网络模型)。
  • 本课程可以帮助您获得以下认证:Predictive Modeling Using SAS Enterprise Miner.

  • 3 Intermediate 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 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 Intermediate 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 Live Web Classroom
    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 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 Live Web Classroom e-Learning
    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.

    Please note: This course is not intended to teach credit risk modeling using SAS. Previous SAS software and SAS Enterprise Miner experience is helpful but not necessary.

    3 Intermediate Live Web Classroom e-Learning
    Data Mining Techniques: Predictive Analytics on Big Data New
    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 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 Advanced e-Learning
    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 Advanced 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 使用SAS Enterprise Miner进行应用分析 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 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 Advanced 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 Advanced Live Web Classroom e-Learning