SAS Visual Statistics
| Titre | Niveau | Types de formation |
|---|---|---|
Summer Fast Track Accessible Analytics 
This course is for those who are working with big data and want to prepare their data, explore their data and model their data in a visual way. Results will be presented in an interactive report. SAS Visual Analytics and SAS Visual Statistics are the tools that are used in the training. This fast track combines the courses SAS Visual Analytics 1, SAS Visual Analytics 2 and SAS Visual Statistics on SAS Viya. |
1 Débutant |   |
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SAS Viya Overview
Cette formation donne un aperçu des applications de SAS Viya et présentecomment chaque application peut être utilisée dans les différentes étapes(Données, découverte de connaissances, déploiement et organisation) du cycleanalytique. |
1 Débutant |   |
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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:
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3 Intermédaire | |
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SAS Visual Statistics : Construction Interactive de Modèles
Ce cours présente SAS Visual Statistics pour la construction de modèles prédictifs. SAS Visual Statistics introduit le concept d'exploration de données et de construction interactive des modèles qui sont essentiels dans le traitement et la modélisation des Big Data. |
3 Intermédaire | |
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Advanced Machine Learning Using SAS Viya
This course teaches you how to optimize the performance of predictive models beyond the basics by implementing various data munging and wrangling techniques. The course continues the development of supervised learning models that begins in the Machine Learning en utilisant SAS Viya course and extends it to ensemble modeling. Running unsupervised learning and semi-supervised learning models is also discussed. In this course, you learn how to do feature engineering and clustering of variables, and how to preprocess nominal variables and detect anomalies. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. Importing and running external models in Model Studio is also discussed, including open-source models. SAS Viya automation capabilities at each level of machine learning are also demonstrated, followed by some tips and tricks with Model Studio. The self-study e-learning includes:
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4 Expert | |
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Introduction to Data Science
The goal of this course is to make analytics approachable and comprehensible. After completing this course, you will be able to perform the main data-related tasks for the citizen data scientist using the point-and-click capabilities of SAS Visual Analytics and SAS Visual Statistics: accessing and manipulating data, exploring data using analytics, and building predictive models. |
1 Débutant | |
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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 | |