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What is SAS Enterprise Guide?

SAS Enterprise Guide offers a convenient method for users to tap into SAS's analytical power through a straightforward point-and-click interface tailored for Windows. This application empowers users to perform analyses independently, combining an extensive array of analytical choices with the robust functionalities of SAS software, all presented within an easy-to-navigate graphical interface. As a result, business analysts are able to derive their own insights and produce reports, which allows IT departments to focus on more critical strategic tasks. Moreover, SAS Enterprise Guide integrates effortlessly with SAS Viya, permitting teams to maintain their current workflows while running SAS programs in the cloud without any need for recoding. This integration capitalizes on the benefits of a modern, cloud-based setup, which provides improved efficiency, scalability, and superior analytical capabilities. Additionally, the software aids users in swiftly accessing data for their analyses, scheduling projects, collaborating on outcomes, and embedding results for repeated use. This includes the application of advanced analytics and a variety of other features offered by SAS, creating a comprehensive toolset for users. Furthermore, this amalgamation of functionalities not only boosts productivity but also encourages a collaborative environment for data analysis within organizations, promoting a culture of shared knowledge and insight generation.

What is SAS Data Science Programming?

Develop and oversee large-scale decision-making processes driven by data, whether in real-time or batch formats. The SAS Data Science Programming approach is tailored for data scientists who prefer a comprehensive programmatic style, enabling them to engage with SAS's analytical tools throughout the full analytics life cycle, which includes stages like data preparation, exploration, and deployment. Identify and illustrate crucial patterns in datasets, which facilitates the generation and sharing of interactive reports and dashboards. Furthermore, utilize self-service analytics to quickly assess potential outcomes, empowering organizations to make well-informed, data-driven choices. Work with your data to create or adjust predictive analytical models using the SAS® Viya® platform. This collaborative framework encourages data scientists, statisticians, and analysts to unite in refining their models iteratively across different segments, ultimately bolstering decision-making grounded in dependable insights. Address complex analytical problems through an intuitive visual interface that adeptly manages all facets of the analytics life cycle, ensuring users can navigate challenges with both ease and accuracy. By adopting this methodology, organizations can significantly improve their strategic decision-making capabilities and drive better overall performance in their operations. Emphasizing collaboration and innovation within analytics will lead to more agile responses to rapidly changing market conditions.

Media

Media

Integrations Supported

SAS Viya

Integrations Supported

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

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

Windows

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

SAS

Date Founded

1976

Company Location

United States

Company Website

www.sas.com/en_us/software/enterprise-guide.html

Company Facts

Organization Name

SAS

Company Location

United States

Company Website

support.sas.com/en/software/data-science-programming.html

Categories and Features

Data Analysis

Not specified

Categories and Features

Data Science

Not specified

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