SAS Enterprise Miner

コース名	レベル	受講形態
実践! ビジネス課題へのアナリティクス活用基礎講座（教師なし学習編）本コースは、SAS Enterprise Guide、SAS Enterprise Minerを用いてビジネス演習課題を解決するワークショップ形式のコースです。ビズネス課題を解決するために、効果的にデータを活用して分析する方法を、SASが提唱するアナリティクス・ライフサイクルのプロセスに沿って、実践的な題材を基に、実際にデータにふれながら学習します。データの準備・探索、分析用データの作成・加工、分析と結果の考察・報告、業務にといった分析プロセス全体の流れを辿りながら、必要なステップを踏み、ポイントをおさえることで、自身でそれぞれの工程のイメージを持って、その作業を実現することができるようになることを目標とします。	1 入門
SAS Enterprise Miner 分析の応用本コースは、SAS Enterprise Minerを利用し、パターン発見（セグメンテーション、アソシエーション、およびシーケンス分析）や予測モデリング（デシジョンツリー（決定木）、回帰、およびニューラル・ネットワーク）といったデータ・マイニングにおける分析手法を紹介します。	3 中級
Credit Risk Modeling 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 中級
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 中級
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 中級
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 中級
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 中級
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 中級
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 中級
Strategies and Concepts for Data Scientists and Business Analysts To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. 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 that data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data. This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience. The self-study e-learning includes: Annotatable course notes in PDF format. Virtual lab time to practice.	3 中級
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 上級
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 上級
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 SAS Enterprise Miner 分析の応用 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 上級
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 上級
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 上級