IBM SPSS Statistics Alternatives for Mac OS X

R is a powerful open-source language and environment for statistical computing, graphics, and data analysis. Widely used by statisticians and data miners for developing statistical software and data analysis.

RStudio is a powerful free and open-source integrated development environment (IDE) tailored for R, the premier language for statistical computing and data analysis. It provides a user-friendly interface that simplifies R coding, debugging, visualization, and reporting workflows, making it an essential tool for data scientists, statisticians, and researchers.

Maple is a powerful mathematical computing environment and multi-paradigm programming language designed for engineers, scientists, educators, and students. It provides a comprehensive suite of tools for symbolic and numeric computation, data analysis, visualization, and application development across numerous domains.

SymPy is a comprehensive Python library for symbolic mathematics. It empowers users to perform advanced mathematical computations, ranging from basic algebra and calculus to more complex concepts like differential equations and geometric algebra, all within the familiar Python environment.

RapidMiner is an end-to-end data science platform that streamlines machine learning workflows from data preparation to model deployment, empowering users to unlock insights and build predictive models without extensive coding.

KNIME (Konstanz Information Miner) is a leading open-source platform for data science. It provides a visual workflow interface that enables users to build, train, and deploy machine learning models and data pipelines without requiring extensive coding expertise.

SOFA Statistics is a user-friendly, open-source statistical package designed for researchers, analysts, and students to easily perform statistical analysis and generate reports without requiring extensive coding knowledge.

Neural Designer is an advanced analytics software platform designed for building and deploying neural network models to solve complex data analysis and predictive modeling tasks. It enables users to extract valuable insights from large datasets, perform forecasting, and make data-driven decisions.