SAS Enterprise Miner

Título Nível Formatos de Treinamento
Applied Analytics Using SAS Enterprise Miner
Esse curso abrange as habilidades necessárias para montar diagramas de fluxo de análise utilizando o amplo conjunto de ferramentas do SAS Enterprise Miner tanto para a identificação de padrões (segmentação, associação e análise de seqüências) quanto para modelagem preditiva (modelos de árvore de decisão, regressão e redes neurais).

3 Intermediário Classroom Live Web Classroom e-Learning
Credit Risk Modeling Business Knowledge Series
Neste curso, os alunos aprendem a desenvolver modelos de risco de crédito no âmbito das recentes diretrizes da Basiléia II e III. O curso oferece uma combinação adequada de compreensão teórica e técnica, bem como detalhes de implementação e prática. Estas são ilustradas por diversos casos de estudo e exercícios.

3 Intermediário 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 Intermediário 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 Avançado 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 Avançado Live Web Classroom e-Learning
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 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 Intermediário e-Learning
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.

3 Intermediário Live Web Classroom e-Learning
Development of Credit Scoring Applications Using SAS Enterprise Miner
Este curso ensina aos alunos como construir Credit Scorecard, do início ao fim, utilizando o SAS Enterprise Miner e metodologias recomendadas por líderes de crédito e especialistas em finanças.

3 Intermediário Classroom Live Web Classroom e-Learning
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 Classroom 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 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.

4 Avançado Live Web Classroom
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 Classroom Live Web Classroom
Neural Network Modeling New
Este curso ajuda à compreender e aplicar dois populares algoritmos de redes neurais artificiais, multi-layer perceptrons e radial basis functions. Tanto os aspectos teóricos e práticos da instalação de redes neurais são cobertos. Especificamente, este curso ensina como escolher uma arquitetura de rede neural adequada, como determinar o método de treinamento pertinentes, e como construir redes neurais personalizadas usando a procedure NEURAL.

4 Avançado Classroom Live Web Classroom e-Learning