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What is SAS Visual Analytics?

An intuitive low-code visual platform enables users to create and deploy customized chatbots that comprehend natural language effectively. Users can obtain text responses, access data, create reports and visualizations, and apply analytics and AI through an easy-to-navigate conversational interface. Integrated within the SAS ecosystem, the platform allows seamless configuration of bots to improve data insight accessibility or connect them to external services for enhanced reach. Gaining insights from data has never been easier than just sending a straightforward message. With the aid of intelligent suggestions and clearly labeled related metrics, users can swiftly pinpoint significant relationships within their data. By harnessing machine learning and natural language explanations, users can discover, visualize, and convey engaging narratives and insights that are both accessible and comprehensible. This allows for a deep investigation into the causes behind various outcomes, exploration of all potential avenues, and the unearthing of hidden opportunities within the data. Moreover, the system automatically identifies key relationships, outliers, clusters, and more, revealing vital insights that can inform strategic actions and decision-making. Ultimately, this groundbreaking approach equips users with the tools to confidently and clearly navigate through intricate data environments and make impactful decisions based on their findings.

What is DataMelt?

DataMelt, commonly referred to as "DMelt," is a versatile environment designed for numerical computations, data analysis, data mining, and computational statistics. It facilitates the plotting of functions and datasets in both 2D and 3D, enables statistical testing, and supports various forms of data analysis, numeric computations, and function minimization. Additionally, it is capable of solving linear and differential equations, and provides methods for symbolic, linear, and non-linear regression. The Java API included in DataMelt integrates neural network capabilities alongside various data manipulation techniques utilizing different algorithms. Furthermore, it offers support for symbolic computations through Octave/Matlab programming elements. As a computational environment based on a Java platform, DataMelt is compatible with multiple operating systems and supports various programming languages, distinguishing it from other statistical tools that often restrict users to a single language. This software uniquely combines Java, the most prevalent enterprise language globally, with popular data science scripting languages such as Jython (Python), Groovy, and JRuby, thereby enhancing its versatility and user accessibility. Consequently, DataMelt emerges as an essential tool for researchers and analysts seeking a comprehensive solution for complex data-driven tasks.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
JMP Statistical Software
Microsoft Azure
Red Hat OpenShift
SAS Viya

Integrations Supported

Apache NetBeans
Eclipse BIRT

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$0
Access to full documentation (examples, Java API documentation, online manual) requires one-time membership payment (20$/user)
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad

Supported Platforms

Android
Windows
Mac
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

SAS

Date Founded

1976

Company Location

United States

Company Website

www.sas.com/en_us/software/visual-analytics.html

Company Facts

Organization Name

jWork.ORG

Date Founded

2005

Company Location

United States

Company Website

datamelt.org

Categories and Features

Data Analysis

Not specified

Data Visualization

Not specified

Categories and Features

Artificial Intelligence

For Healthcare
Image Recognition
Machine Learning
Natural Language Processing
Predictive Analytics

Data Analysis

Data Discovery
Data Visualization
Predictive Analytics
Regression Analysis
Statistical Modeling
Text Analytics

Data Mining

Data Extraction
Data Visualization
Machine Learning
Predictive Modeling
Statistical Analysis
Text Mining

Data Visualization

Analytics
Simulation Models
Visual Discovery

Deep Learning

Convolutional Neural Networks
Document Classification
ML Algorithm Library
Model Training
Self-Learning
Visualization

Education

Not specified

Neural Network

Not specified

Statistical Analysis

Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Simulation
Survival Analysis
Time Series
Visualization

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