SAS Enterprise Miner

Título	Nível	Formatos de Treinamento
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 Intermediário
Managing SAS® Analytical Models Using SAS® Model Manager Version 14.2 Este curso se concentra nas seguintes áreas: gestão de fontes de dados do Model Manager, criar um projeto no Model Manager, importar modelos para o Model Manager, usar o Model Manager Query Utility, criação de tarefas de pontuação, exportar modelos e projetos para o repositório SAS e criar e configurar versão de ciclos de vida. Este curso também ensina como criar e gerar relatórios de modelos de comparação, publicar e implantar modelos do Model Manager, criar modelos de acompanhamento de relatórios no Model Manager e criar relatórios definidos pelo usuário.	3 Intermediário
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 Intermediário
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 Intermediário
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 Intermediário
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 Intermediário
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 Intermediário
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. 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 Intermediário
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 Intermediário
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 Avançado
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 Avançado
Decision Tree Modeling Este curso abrange modelos preditivos estruturados em árvore e a metodologia para o growing, pruning, e avaliar árvores de decisão. Além disso, este curso aborda muitos dos usos auxiliares de árvores tais como análise exploratória de dados, redução de dimensão e de imputação valores faltantes™.	4 Avançado
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 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 Avançado
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.	4 Avançado