SAS Enterprise Miner

Titre Niveau Types de formation
Customer Segmentation Using SAS Enterprise Miner Business Knowledge Series
No marketing strategy can be effective without segmentation. While the concept of segmentation is deceptively simple, in practice it is extremely difficult to execute. Emphasizing practical skills as well as providing theoretical knowledge, this hands-on, comprehensive course covers segmentation analysis in the context of business data mining. Topics include the theory and concepts of segmentation, as well as the main analytic tools for segmentation: hierarchical clustering, k-means clustering, normal mixtures, RFM cell method, and SOM/Kohonen method. The course focuses more on practical business solutions rather than statistical rigor. Therefore, business analysts, managers, marketers, programmers, and others can benefit from this course.

3 Intermédaire Classroom
SAS Enterprise Miner: étude de cas
Une journée supplémentaire pour pratiquer sur le logiciel SAS Enterprise Miner ! Vous aurez la possibilité d’utiliser le logiciel SAS Enterprise Miner en traitant un cas concret.
• Vous avez suivi la formation SAS® Enterprise Miner ™: applications des techniques de Data Mining, et souhaitez valider vos acquis en mettant en œuvre la méthodologie SEMMA. Au travers d’une étude de cas, construisez votre analyse en appliquant toutes les étapes présentées dans la formation SAS® Enterprise Miner ™: applications des techniques de Data Mining. Valider ainsi votre compréhension et l’interprétation des résultats.
• Vous souhaitez passer la certification « certification Data Mining avec SAS Enterprise Miner», participez à cette journée pour vous entrainer et réviser les thèmes essentiels.

0 Sans niveau Classroom
SAS Enterprise Miner : Développement de Scorecard pour le risque de crédit

3 Intermédaire Classroom
Les nœuds de Data Mining de SAS Enterprise Miner High-Performance
Cette formation vous présente les similitudes et les différences entre les nœuds High-Performance de SAS® Enterprise MinerTM 12.3 et les nœuds classiques. Vous comparerez l’analyse des données faite avec un environnement SAS traditionnel et celle réalisée dans un environnement High Performance.

3 Intermédaire Classroom Live Web Classroom
SAS Enterprise Miner : applications des techniques de Data Mining

Vous souhaitez optimiser votre ciblage Marketing, développer vos ventes, détecter les comportements frauduleux… Les modèles prédictifs permettent de répondre à ces attentes. Ce cours vous présente comment implémenter et industrialiser ces modèles avec la méthodologie SEMMA du logiciel SAS® Enterprise MinerTM.

3 Intermédaire Classroom Live Web Classroom e-Learning
Détecter la fraude grâce aux méthodes d’analyses supervisées, non supervisées et aux réseaux sociaux Business Knowledge Series

4 Expert Classroom Live Web Classroom e-Learning
Gérer vos modèles grâce à SAS Model Manager
Vous souhaitez suivre le cycle de vie de vos modèles (SAS STAT, SAS Enterprise Miner, PMML) de prévision (credit scoring, ciblage marketing, détection fraude, développement ventes, …) dans une application partagée et structurée, afin de disposer à tout moment d’un bilan des performances des vos prédictions.

3 Intermédaire Classroom Live Web Classroom e-Learning
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 Intermédaire Classroom Live Web Classroom 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.

The e-learning format of this course includes Virtual Lab time to practice.

3 Intermédaire Classroom Live Web Classroom 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 Intermédaire 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 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 Intermédaire Classroom Live Web Classroom e-Learning
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 Intermédaire Classroom Live Web Classroom e-Learning
SAS Enterprise Miner : modèles prédictifs - Techniques avancées
Il est fréquent de vouloir prédire si un client va répondre positivement à un mailing mais ausside prédire ensuite combien il va dépenser en achats de produits.Ce cours présente au travers de SAS® Enterprise Miner™ la modélisation «Two Stage».Cette modélisation consiste à modéliser conjointement une variable qualitative et une variable quantitative.

4 Expert Classroom Live Web Classroom e-Learning
SAS Enterprise Miner : construction des arbres de décision

4 Expert Classroom Live Web Classroom e-Learning
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 Classroom Live Web Classroom e-Learning
Advanced Analytics in a Big Data World New 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 website 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. The instructor will extensively report on both his research and consulting experience in the field. References to background material such as selected papers, tutorials, and guidelines are also provided.

Note: This course was formerly titled Advanced Analytics for Customer Intelligence Using SAS.

4 Expert 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 Intermédaire Live Web Classroom e-Learning