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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.

What is IBM SPSS Modeler?

IBM SPSS Modeler stands out as a premier visual data-science and machine-learning platform, aimed at assisting businesses in speeding up their realization of value by automating routine tasks typically handled by data scientists. Organizations globally utilize this tool for various functions, including data preparation, exploration, predictive analytics, and the management and deployment of models. Additionally, machine learning capabilities are leveraged to extract value from data assets. By optimizing data into the most suitable formats, IBM SPSS Modeler enhances the accuracy of predictive modeling. Users can efficiently analyze data with just a few clicks, pinpoint necessary corrections, filter out irrelevant fields, and generate new features. The software's robust graphics engine plays a crucial role in visualizing insights effectively, while the intelligent chart recommender feature identifies the most suitable charts from an extensive selection to effectively communicate findings. This streamlined approach not only simplifies data analysis but also fosters a deeper understanding of business trends.

Media

Media

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

Integrations Supported

Apache Spark
Hadoop
IBM SPSS Amos
IBM SPSS Statistics
Python
R
TensorFlow
icCube

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Linux

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

SAS

Company Location

United States

Company Website

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

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/products/spss-modeler

Categories and Features

Data Science

Not specified

Statistical Analysis

Not specified

Categories and Features

Data Modeling

Not specified

Data Science

Not specified

RFM Analysis

Not specified

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