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What is Scilab?

Numerical analysis, often referred to as scientific computing, emphasizes methods for approximating solutions to various mathematical problems. Scilab offers a wide range of graphical functions that enable users to visualize, annotate, and export data, along with a multitude of options for crafting and customizing different plots and charts. Serving as a high-level programming language tailored for scientific applications, Scilab accelerates the prototyping of algorithms while reducing the complications associated with lower-level languages such as C and Fortran, where challenges like memory management and variable declarations can complicate workflows. In Scilab, intricate mathematical calculations can frequently be articulated in a handful of lines of code, while other programming languages may require much more extensive coding efforts. Moreover, Scilab comes equipped with advanced data structures like polynomials, matrices, and graphic handles, and it offers a user-friendly development environment that boosts productivity and simplifies usage for both researchers and engineers. Consequently, Scilab not only streamlines the scientific computing process but also broadens access to these tools for a larger audience, making complex computations more manageable. Furthermore, its extensive library of built-in functions enhances the capacity for users to tackle a variety of mathematical tasks effectively.

What is SAS Visual Statistics?

SAS Visual Statistics fosters collaborative data exploration, allowing multiple users to interactively create and refine predictive models. Data scientists and statisticians can apply the most appropriate analytical techniques to derive insights at an intricate level. As a result, insights can be discovered at impressive speeds, leading to new revenue growth opportunities. This platform permits the construction and optimization of models targeted at specific demographics or segments, while simultaneously exploring various scenarios. Such capabilities motivate users to raise numerous what-if questions to improve outcomes. Moreover, results can be operationalized through automatically generated score code, streamlining application processes. Users can visually manipulate the data by adding or modifying variables, removing outliers, and more, which enables them to instantly evaluate how changes affect the model's predictive accuracy, facilitating rapid adjustments. Data science teams benefit from the flexibility of working in their preferred programming languages, thereby maximizing their skill set. Ultimately, SAS Visual Statistics unifies all analytical resources into a holistic solution for data-driven decision-making. This integration creates an environment that nurtures innovation and expands the horizons of data analysis, enabling teams to push the limits of their analytical capabilities. Furthermore, the collaborative features of the platform enhance teamwork and knowledge sharing among users, driving better results through collective expertise.

Media

Media

Integrations Supported

Overleaf

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
Red Hat OpenShift
SAS Analytics for IoT
SAS Data Science Programming
SAS Visual Data Science
SAS Visual Data Science Decisioning
SAS Visual Machine Learning
SAS Viya

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS
Windows
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

On-Site Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

Scilab Enterprises

Company Website

www.scilab-enterprises.com

Company Facts

Organization Name

SAS

Company Location

United States

Company Website

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

Categories and Features

Statistical Analysis

Association Discovery
Compliance Tracking
File Storage
Statistical Simulation
Visualization

Categories and Features

Data Science

Not specified

Statistical Analysis

Not specified

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