IMPORTANT: Il benessere, la salute e la sicurezza di tutti voi e di tutti noi rappresentano una priorità per SAS. A tutela di ciò e in ottemperanza a quanto previsto dalle misure stabilite dal Governo in termini di prevenzione della diffusione di COVID-19 e da direttiva della nostra casa madre, SAS Institute S.r.l., per tutto il primo semestre 2020 i corsi a calendario pubblico SAS Italia previsti presso tutte le sedi SAS (Milano, Roma, Venezia Mestre) vengono confermati nell’attuale pianificazione ma svolti in modalità Live WEB, ovvero con il docente, di lingua italiana, che svolge il corso in persona, collegato da remoto con video camera e live. Rimangono inalterati i contenuti, la durata, l’interazione docente / partecipanti, la struttura del corso e l'utilizzo dei laboratori virtuali SAS per esercitarsi e svolgere la parte pratica. Tutti gli esami di certificazione presso tutte le sedi SAS sono sospesi. Oltre alla modalità Live Web sono disponibili i corsi anche nella versione E-Learning. Per ulteriori informazioni Contattaci.



SAS Viya

Titolo Livello Formato del corso
SAS Viya Enablement Free e-learning
The SAS Viya Enablement training provides access to information about SAS Viya and the related products. This free e-learning provides access to a collection of information, including videos and documentation as well as links to additional product information. This is not a traditional e-learning course.

1 Base e-Learning
SAS Visual Investigator: Building the Interface
This course teaches you how to develop an application interface for monitoring financial crimes.

2 Principiante Live Web Classroom e-Learning
Supervised Machine Learning Procedures Using SAS Viya in SAS Studio
This course combines data exploration, visualization, data preparation, feature engineering, sampling and partitioning, model training, scoring, and assessment. It covers a variety of statistical, data mining, and machine learning techniques performed in a scalable and in-memory execution environment. The course provides theoretical foundation and hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The course includes predictive modeling techniques such as linear and logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, and factorization machine.

3 Intermedio Corso in Aula Live Web Classroom
SAS Viya Overview: Architettura e Soluzioni per lo Sviluppo di ambienti analitici (italian course) New
SAS Viya è una piattaforma “cloud-ready” “in-memory” aperta ed integrata con i più diffusi linguaggi di programmazione che unifica ed integra gli ambienti di amministrazione, gestione, sviluppo ed esecuzione di tutti i processi di analisi dei dati e di visualizzazione dei risultati attuati in un’organizzazione. Comprende diversi prodotti, alcuni in versione inedita per la piattaforma, altri già presenti nella precedente versione SAS 9. Il corso ha l’obiettivo di mostrare l’ambiente della piattaforma e le principali interfacce applicative e di programmazione.

3 Intermedio Corso in Aula
Neural Networks: Essentials
This course combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application. During the course, you programmatically build a neural network and discover how to adjust the model’s essential parameters to solve different types of business challenges. You implement early stopping, build autoencoders for a predictive model, and perform an intelligent automatic search of the model hyperparameter values. The last lesson introduces deep learning. You gain hands-on practice building neural networks in SAS 9.4 and the cutting-edge, cloud-enabled in-memory analytics engine for big data analytics, SAS Viya.

3 Intermedio Live Web Classroom
SAS Viya and Python Integration for Machine Learning
In this course, you learn to use the Python API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar Python functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You then learn to download results to the client and use native Python syntax to compare models.

3 Intermedio Corso in Aula Live Web Classroom
SAS® Viya® and R Integration for Machine Learning
In this course, you learn to use the R API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar R functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You then learn to download results to the client and use native R syntax to compare models.

3 Intermedio Corso in Aula Live Web Classroom
SAS Risk Modeling: Using the Solution
SAS Risk Modeling enables you to quickly and efficiently create analytical base tables that are used for developing credit scoring models. In this course, you learn how to create analytical base tables by calculating variables from different sources. Using SAS Model Studio to develop an application scorecard is demonstrated and practiced. By the end of this course, you will be comfortable working in Risk Modeling workspaces that are used for implementing models and monitoring their performance.

3 Intermedio e-Learning
SAS Visual Statistics in SAS Viya: Interactive Model Building
This course introduces SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data. This course is appropriate for users of SAS Visual Analytics in SAS Viya 3.5.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual Lab time to practice.

3 Intermedio Live Web Classroom e-Learning
Supervised Machine Learning Pipelines Using SAS Viya
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. A series of demonstrations and exercises is used to reinforce all the concepts and the analytical approach to solving business problems. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.


The e-learning version of this course provides access to SAS Viya for Learners, which enables students to use the software to complete the practices.

3 Intermedio Corso in Aula Live Web Classroom e-Learning
Self-Service Data Preparation in SAS Viya
This course provides an overview of the analytic data preparation capabilities of SAS Data Preparation in SAS Viya. These self-service data preparation capabilities include bringing data in from a variety of sources, preparing and cleansing the data to be fit for purpose, analyzing data for better understanding and governance, and sharing the data with others to promote collaboration and operational use.

3 Intermedio Corso in Aula Live Web Classroom e-Learning
Programming for SAS Viya
This course is for users who need to modify existing Base SAS programs that will execute in SAS Viya. This course leverages the power of SAS Cloud Analytic Services (CAS) to access, manage, and manipulate in-memory tables. This course is not intended for beginning SAS software users.

3 Intermedio Corso in Aula Live Web Classroom e-Learning
Deep Learning Using SAS Software
This course introduces the essential components of deep learning. Participants learn how to build deep feedforward, convolutional, and recurrent networks. The neural networks are used to solve problems that include traditional classification, image classification, and time-dependent outcomes. The course also presents practical methods used to enhance training data to produce better models. Lastly, a method for efficiently searching hyperparameters is described.

3 Intermedio Corso in Aula Live Web Classroom e-Learning
Tree-Based Machine Learning Methods in SAS Viya
Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This course covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya. The course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forest and gradient boosting models. The course also explains isolation forest (an unsupervised learning algorithm for anomaly detection), deep forest (an alternative for neural network deep learning), and Poisson and Tweedy gradient boosted regression trees. In addition, many of the auxiliary uses of trees, such as exploratory data analysis, dimension reduction, and missing value imputation, are examined, and running open source in SAS and running SAS in open source are demonstrated.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

4 Esperto e-Learning