Altair Analytics Workbench™

Powerful and user-friendly interactive development environment

Many organizations support analytical silos for their data engineers, analytics, scientists, and statisticians. These often require large — and different — toolsets to help each profile deal effectively with the various stages of the analytical lifecycle. The software lets you unify these silos, improve productivity, and reduce costs by providing a single platform where all users can connect, prepare, discover, and model any data.

Altair Analytics Workbench is a sophisticated coding environment that’s ideal for developing models and programs written in the SAS language. With it, developers can include Python, R, or SQL code in their SAS language programs, and it requires no third-party software to run SAS language programs. The platform also provides a drag-and-drop workflow where users can develop models and programs without writing any code.

Why Altair Analytics Workbench?

Empowers Users of Mixed Abilities and Skillsets

Analytics Workbench fulfills the needs of data engineers, data analysts, data modelers, data scientists, and citizen data scientists. People with no coding skills can use the software’s visual workflow to extract and transform data from a variety of disparate sources and produce spreadsheets and reports, while expert users can perform advanced analytics tasks using the platform’s sophisticated coding environment, including data prep, exploring, profiling, data visualization, predictive modeling with decision trees, regression, scorecards, and clustering/segmentation analysis, and model validation.

Maintain Existing SAS Language Programs and Develop New Ones

Analytics Workbench is powered by Altair SLC™ to run workflows, programs, and models. It’s a complete integrated development environment (IDE) for handling your existing code library and developing new programs written in the SAS language. The software includes a sophisticated code editor, code templates, the ability to run your programs and explore the resulting logs, libraries, datasets and other generated output, project management with code history facilities, and optional integration to GIT version control systems.

Mix the SAS Language with Python, R, and SQL

Users who want to bridge existing SAS language needs with open-source languages can embed Python, R, and SQL code blocks in workflows or SAS language programs. Users can also exchange and process data between the Python, R, SQL and SAS language segments of your programs and workflows.

Key Features

Visual Workflow Environment

Build workflows with drag-and-drop interactive blocks to provide the perfect combination of low-level data engineering facilities for retrieving, blending, and preparing data for analysis, along with machine learning features that let you build, explore, and validate reproducible predictive models. Enhance workflows with programmable blocks coded in the languages of SAS, SQL, Python, and R.

Robust Coding Environment

Use a modern integrated development environment (IDE) to create, maintain, and run programs, and to explore data, results, and logs. Analytics Workbench’s coding environment focuses on SAS language programming but also lets users incorporate SQL, Python, and R code within SAS language programs, which can easily exchange data between Python, R, SQL, and SAS language modules.

Simple Data Discovery Capabilities

Analytics Workbench offers a robust range of functions that empower users to fully understand their source data and uncover new insights, including profiling, automated quality checking, validation, and automatic variable reporting.

No-Code Machine Learning Model Development

Analytics Workbench features machine learning support for supervised and unsupervised learning, including decision trees, clustering, regression analysis and neural networks. Explore, build, and test machine learning models with workflow blocks and automatically generate error-free code for production use.

Quickly Compare Model Performance

Build and validate different types of models against the same test data and then use Analytics Workbench’s no-code model comparison tool to identify the best model for you with comparison charts that include Receiver Operating Characteristics (ROC), Kolmogorov–Smirnov (KS), cumulative gain, and lift.

Easy-to-Build Scorecards

Use our simple visual development tools to build predictive, behavioral, and application scorecards to help with variable selection, training, evaluation, and model validation. Automatically extract error-free and ready to deploy scorecard code for use in production.

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