We’re here to help. Alors que nous faisons face au COVID-19 ensemble, notre engagement envers vous reste fort. Si vous souhaitez développer des compétences essentielles à votre carrière, nous vous invitons à suivre nos formations en ligne gratuites ou à vous inscrire à des cours en classe connectée. Suivez le cours avec un formateur SAS et des machines virtuelles pour pratiquer en temps réel, comme dans une salle de formation.



SAS Enterprise Miner

Titre Niveau Types de formation
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
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
Text Analytics and Sentiment Mining Using SAS Business Knowledge Series
Big data: it's unstructured, it's coming at you fast, and there's a lot of it. In fact, the majority of big data is unstructured and text oriented, thanks to the proliferation of online sources such as blogs, e-mails, and social media. While the amount of textual data are increasing rapidly, businesses' ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. No marketing or customer intelligence program can be effective today without thoroughly understanding how to analyze textual data. Emphasizing practical skills as well as providing theoretical knowledge, this hands-on course takes a comprehensive look at how to organize, manage, and mine textual data for extracting insightful information from large collections of documents and using such information for improving business operations and performance.

3 Intermédaire Classroom
Strategies and Concepts for Data Scientists and Business Analysts Business Knowledge Series
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 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.

3 Intermédaire Classroom Live Web Classroom
Big Data, Data Mining et Machine Learning Business Knowledge Series
Cette formation introduit les concepts du calcul analytique et du Data Mining dont la modélisation prédictive. Divers sujets sont couverts allant d’une description des environnements informatiques modernes à la mise en œuvre de la méthodologie du Data Mining.

Vous bénéficierez ainsi d’une introduction aux algorithmes de Data Mining, à la segmentation, au Data Mining appliqué aux séries chronologiques et au Text Mining. Ces concepts sont illustrés au travers d’exemples concrets.

Vous apprendrez à :
  • Utiliser la méthodologie du Data Mining
  • Appliquer des algorithmes de modélisation de pointe sur vos données
  • Mettre en œuvre les bonnes pratiques pour développer et maintenir des modèles analytiques.
  • Explorer comment créer de la valeur à l’aide de l’analytique
  • Evaluer les différents modèles de machine Learning.
  • Expliquer de manière simple le concept de data Mining et les méthodes de machine Learning.
  • Utiliser des données non structurées dans votre modélisation.
  • Construire des segments pour vos applications métiers.

    		• 3 Intermédaire Classroom Live Web Classroom
    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 Intermédaire 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.

    		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 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 Classroom Live Web Classroom e-Learning
    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 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 Live Web 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
    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 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 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 Live Web Classroom e-Learning
    SAS® Enterprise Miner™ : Développement de Scorecard pour le risque de crédit

    		3 Intermédaire 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™ : 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
    SAS® Enterprise Miner™ : construction des arbres de décision

    		4 Expert Classroom Live Web Classroom
    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 Expert Classroom Live Web Classroom e-Learning