Scilab for Mac

Scilab, developed by Scilab Enterprises, is a free science app available for Windows and Mac.

Technical

Title

Scilab

Requirements

Windows
Mac

Language

English

Available languages

German
English
Spanish
French
Italian
Polish
Portuguese

License

Free

Latest update

2025-12-28

Author

Scilab Enterprises

Scilab for other platforms

Scilab for Windows

Other Useful Business Software

Train ML Models With SQL You Already Know

BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.

Start Free

Rate This App

User Reviews

Be the first to post a review of Scilab!

Related Software

Scilab

Scilab is a free, open-source high-level scientific computing environment that provides a programming language and a large library of mathematical routines for numerical computation. It supports solving linear and nonlinear systems, ODEs, numerical integration and differentiation, matrix manipulation, optimization (including linear, nonlinear and quadratic), and creation of publication-quality 2D/3D plots and animations for rapid prototyping, data analysis, and algorithm development. It also includes a graphical modeling and simulation tool for building and testing hybrid dynamical system models from reusable blocks. Scilab is well suited to students, researchers, engineers, and educators working on simulation, scientific visualization, and application development. It is distributed under the CeCILL free software license; Maxima is a commonly recommended free alternative for users focused mainly on symbolic computation.

MATLAB

MATLAB is a specialized environment for numerical and scientific computing that combines numerical computation, data visualization, and a programming language optimized for matrix and array operations. It provides an interactive workspace for running commands, editing and debugging scripts, prototyping algorithms, running simulations, and producing publication-quality visualizations; the Live Editor lets users mix executable code, formatted text, equations, and output in a single document. Common native file types include .m scripts/functions and .mat binary containers for saving variables and arrays, and typical applications cover numerical linear algebra, signal and image processing, control design, algorithm development, and data analysis. The tool is used by students, engineers, scientists, applied mathematicians, and data analysts who rely on its strengths in array manipulation, rapid plotting, modeling, and algorithm development. A wide ecosystem of add-on toolboxes extends functionality and enables interoperability with other tools. MATLAB is feature-rich and mature, with extensive documentation and examples to ease learning, though it generally requires an investment of time and, for many users, commercial licensing.

SciDAVis

SciDAVis is a free scientific data analysis and visualization tool that combines interactive plotting with numerical and statistical routines to help users explore data, perform calculations, and produce publication-ready figures. It supports a variety of common data formats, offers quick-reference guides and community resources for learning, and provides flexible plotting options including 2D and 3D graphs, templates, and multi-panel layouts. The application includes data transformation, filtering, and combination tools, along with statistical summaries, hypothesis-testing utilities, and curve-fitting/regression capabilities for modeling relationships. Its approachable interface and integrated set of plotting plus analysis tools make it suitable for students, researchers, and anyone needing a cost-free solution for scientific plotting and data analysis.

Mathematica

Mathematica is a mature, unified technical-computing platform that combines symbolic and numeric computation, visualization, and specialized toolsets. It leverages optimized algorithms and built-in routines for high performance on large or computationally intensive problems, and provides automation and repeatable workflows to accelerate projects and reduce manual overhead. It supports interactive, notebook-style documents for combining code, results, and commentary to produce reproducible reports, and includes extensive documentation and examples to shorten the learning curve. Common uses include interactive visualizations and modeling, data science and machine learning, image analysis, simulation, and algorithm development.

Anaconda Distribution

Anaconda Distribution is a free, full-featured platform for scientific computing with Python and R that streamlines installing, managing, and running libraries and their dependencies. It bundles a package and environment manager, interactive tools like Jupyter, R support, IDEs and a curated collection of libraries for statistics, visualization, numerical computing and machine learning to accelerate development and reproducible workflows. It is well suited to applied data analysis, model development and reproducible research, particularly for users who want an out-of-the-box setup with many common packages available, consistent environments for collaboration, and support for combining Python and R within a single distribution.

DigitizeIt

DigitizeIt extracts numerical data from charts and plots in images by letting users define axes, select chart elements, and enter calibration points to recover underlying data points, which can be saved as plain text or exported in scientific notation. It accepts common raster image formats for input. It’s useful for reusing values for analysis or publication, but the interface can feel technical and achieving precise results may require patience and some familiarity with math or graph interpretation. For lighter needs, consider free online digitizing services as an alternative.

AquaTerm

AquaTerm is a graphics renderer designed to help scientists, engineers, and data analysts turn numerical results into clear, high-resolution plots and diagrams. It offers fine-grained styling (colors, line styles, labels, layout), efficient handling of large or complex datasets, configurable export presets, and the ability to produce publication-ready images. It supports vector and raster export formats (SVG, EPS, PDF, TIFF, PNG, and bitmap), and is well suited to users working with mathematical models, experimental results, or engineering simulations who need precise, customizable graphics. A free alternative with similar visualization capabilities is DDPM Free.

Octave

GNU Octave is a free, open-source scripting language and interactive environment for numerical computation, offering MATLAB-like syntax that eases reuse of existing scripts. It provides robust linear algebra and numerical routines, tools for polynomial and differential-equation problems, and the ability to extend performance-critical parts with optimized external code; it’s well suited to scientists, engineers, and mathematicians, though newcomers should expect a learning curve. Octave is also described as a compact point-and-click horror-adventure that mixes exploration, environmental storytelling, puzzles, and occasional action. Moody visuals and atmospheric sound design build tension, while puzzles that demand observation and logic deepen immersion, making it appealing to players who enjoy narrative-focused, investigative horror experiences.

Gretl

gretl is a free, open-source application for econometric and time-series analysis that provides a broad set of estimation and modeling tools, including time-series methods, single-equation and system estimators, models for limited dependent variables, count and duration models, and panel-data techniques. It offers routine diagnostics and information criteria for model comparison, publication-ready output and plotting, and facilities for inspecting residuals and other fit measures. The software is extensible via a native scripting language and community-contributed packages, and supports data exchange and interoperability with other analysis tools. Its combination of built-in estimators, time-series procedures, scripting, visualization and documentation makes it suitable for teaching, research and routine empirical work.

R-project

R is a programming environment for statistical computing and graphics that helps researchers organize, analyze, visualize, and reproduce data-driven work. It offers a vast ecosystem of packages for specialized analyses (including bioinformatics), strong support for modeling and machine learning, time-series and classical statistics, and publication-quality graphics with fine-grained control. Because it is open and extensible, users can inspect or extend functionality to fit specialized workflows and build reproducible pipelines and project-centered workspaces. This environment is best suited to advanced students and researchers with a solid grounding in statistics and some programming experience. Paired with an integrated development environment, it streamlines coding, debugging, and project organization, but it does require a learning investment and careful statistical practice to avoid misinterpretation.

Arm Performance Libraries

This toolkit provides highly tuned core math routines—linear algebra, transforms, FFTs and similar primitives—optimized to leverage CPU-specific features to improve execution time, numerical precision, stability, and throughput for computation‑heavy workloads. Developers can use it to accelerate math‑heavy code paths and improve overall runtime behavior. It benefits software engineers and researchers working on machine learning, simulation, signal processing, and high‑performance computing. Alternatives include Eigen, OpenBLAS, and FFTW. Integrate the library where numerical kernels are hotspots, and use benchmarking and profiling to confirm real‑world gains; test across target CPU models to ensure compatibility and expected speedups.

JupyterLab App

JupyterLab App is an interactive environment for data analysis and development that combines notebooks, scripts, and data files in a single adaptable workspace. It offers a multi-document interface, a modular plugin architecture for extensions, and real-time collaborative editing and interactive sessions to keep related files and activities organized. It natively handles Julia, Python, and R, making it useful for data exploration, machine learning experiments, and scientific computing by integrating code, narrative, and visual output to speed iteration. For users seeking benchmarking or graphics-focused capabilities, GFXBench GL Free is an alternative option.

Report inappropriate content