SAS Enterprise Miner

標題	等級	培訓格式
Survival Data Mining Using SAS Enterprise Miner Software 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 Advanced
Development of Credit Scoring Applications Using SAS Enterprise Miner 本課程主要介紹信用評分卡建置方法與流程，提供演練情境設計進行學員實機上線與操作，以達到立即的學習效果。	3 Intermediate
Advanced Predictive Modeling Using SAS Enterprise Miner 本課程分兩大部分，第一部分介紹不同模型中的變數降維方法，第二部分介紹進階預測模型建置方法，包含規則歸納與雙階段預測模型，以及高效能資料採礦模組中的隨機森林與支持向量機模型。除此之外，課程中也會介紹EM 與外部模型之整合。	4 Advanced
Decision Tree Modeling 本課程為決策樹模型的主題課程，介紹決策樹預測模型演算法，包含成長方式、修枝方式與模型評估。除此之外，課程同時介紹如何利用決策樹當輔助工具進行探索性資料分析、變數複雜度縮減與遺漏值插補。	4 Advanced
Applied Analytics Using SAS Enterprise Miner 本課程介紹如何利用Enterprise Miner 進行有效的資料採礦。課程內容主要分為兩大部分，一為非監督式分析，介紹內容有：分群分析、關聯分析以及序列分析；一為監督式預測模型，內容包含有：決策樹、迴歸模型以及類神經網路模型建置方法。	3 Intermediate
Neural Network Modeling 本課程為類神經網路模型的主題課程，透過SAS Enterprise Miner介紹兩種常見的類神經網路演算法：多層感知器 (MLP)與徑向基函數核 (RBF kernel)。除了理論介紹外，課程同時涵蓋實作議題的討論，例如如何選擇適當的類神經網路架構，如何在分散式架構中執行類神經網路等。	4 Advanced
Advanced Analytics in a Big Data World 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
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
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
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
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
Fraud Detection Using Descriptive, Predictive, and Social Network Analytics 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
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
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
Credit Risk Modeling 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