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What is SAS Visual Machine Learning?

Employ a comprehensive set of SAS tools to access, handle, analyze, and present data in visual formats. By using SAS Visual Machine Learning, organizations can significantly boost their analytical skills through integrated machine learning and deep learning functionalities that improve visualization and reporting methods. This strategy empowers users to visually identify and reveal significant connections within their data sets. Furthermore, the platform enables the development and dissemination of interactive reports and dashboards, while also allowing for self-service analytics that quickly assess possible outcomes, encouraging more informed, data-driven decision-making. Users have the capability to explore their data deeply and build or adjust predictive models in the SAS® Viya® environment. Enhanced collaboration among data scientists, statisticians, and analysts allows for the ongoing refinement of models tailored to particular segments or demographics, ensuring that decisions are made based on accurate insights. Additionally, the user-friendly visual interface streamlines the resolution of complex analytical issues, effectively managing all aspects of the analytics lifecycle while fostering a cooperative atmosphere for all participants. This collaborative framework not only enhances the efficiency of the analytical process but also leads to more innovative and effective solutions in data interpretation.

What is Orange?

Leverage open-source machine learning platforms and data visualization methods to construct dynamic data analysis workflows in a visually appealing manner, drawing on a diverse array of resources. Perform basic data evaluations complemented by meaningful visual representations, while exploring statistical distributions through techniques such as box plots and scatter plots; for more intricate analyses, apply decision trees, hierarchical clustering, heatmaps, multidimensional scaling, and linear projections. Even complex multidimensional datasets can be efficiently visualized in 2D using clever attribute selection and ranking strategies. Engage in interactive data exploration to facilitate rapid qualitative assessments, enhanced by intuitive visualizations. The accessible graphical interface allows users to concentrate on exploratory data analysis rather than coding, while smart defaults support the swift development of data workflows. Simply drag and drop widgets onto your canvas, connect them, import your datasets, and derive insightful conclusions! In teaching data mining principles, we emphasize demonstration over mere explanation, and Orange stands out in making this method both effective and enjoyable. This platform not only streamlines the process but also significantly enhances the educational experience for users across various expertise levels. By integrating engaging elements into the learning process, users can better grasp the complexities of data analysis.

Media

Media

Integrations Supported

Microsoft 365
Microsoft Excel
Microsoft Outlook
Microsoft PowerPoint
Microsoft Word
SAS Visual Statistics
SAS Viya

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

Windows
Mac

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Online Training
On-Site Training

Company Facts

Organization Name

SAS

Company Location

United States

Company Website

support.sas.com/en/software/visual-machine-learning.html

Company Facts

Organization Name

University of Ljubljana

Company Location

Slovenia

Company Website

orange.biolab.si

Categories and Features

Data Visualization

Not specified

Machine Learning

Not specified

Categories and Features

Data Mining

Not specified

Data Visualization

Dashboard Creation
Visual Discovery

Machine Learning

Not specified

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