SAS Enterprise Miner

名称 水平 培训形式
Applied Analytics Using SAS Enterprise Miner New

3 Intermediate Classroom 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 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 Intermediate 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 Intermediate 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 Expert 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 Expert e-Learning
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 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 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
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
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 e-Learning
Development of Credit Scoring Applications Using SAS Enterprise Miner
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 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
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 Expert Live Web Classroom
使用SAS Enterprise Miner进行高级预测建模
本课程使用 SAS Enterprise Miner包含了进阶主题,包括超出基本课程的如何优化预测模型的性能等。该课程继续使用SAS Enterprise Miner应用分析中的预测模型开发,例如,通过使用两阶段建模节点,另外,还介绍了一些最新的建模节点和最新的变量选择方法。小贴士:SAS Enterprise Miner高效工作的方法完成课程。

4 Expert 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 Expert Live Web Classroom e-Learning