MATLAB vs Enthought

Struggling to choose between MATLAB and Enthought? Both products offer unique advantages, making it a tough decision.

MATLAB is a Development solution with tags like matrix-manipulation, numerical-computing, visualization, algorithms.

It boasts features such as Matrix and vector computations, 2D and 3D plotting and visualization, Statistical analysis and machine learning, Image processing and computer vision, Modeling, simulation and prototyping, App and algorithm development, Big data analytics and predictive analytics, Data acquisition and measurement and pros including Powerful built-in math and graphics functions, Wide range of toolboxes for domain-specific tasks, Interoperability with C/C++, Java, Python, and other languages, Can handle large data sets and computations efficiently, Extensive visualization and debugging capabilities, Large user community and available resources.

On the other hand, Enthought is a Ai Tools & Services product tagged with python, data-science, analytics, deployment.

Its standout features include Enthought Deployment Manager for deploying Python environments, Canopy Python distribution with scientific and analytic packages, Training and support services for Python and data science, Platform for building and deploying analytics web applications, and it shines with pros like Eases Python environment management and deployment, Comes with many pre-installed scientific and data science packages, Good technical support available, Integrated web framework for building analytics apps.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

MATLAB

MATLAB

MATLAB is a proprietary programming language and interactive environment for numerical computation, visualization, and programming. It allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.

Categories:
matrix-manipulation numerical-computing visualization algorithms

MATLAB Features

  1. Matrix and vector computations
  2. 2D and 3D plotting and visualization
  3. Statistical analysis and machine learning
  4. Image processing and computer vision
  5. Modeling, simulation and prototyping
  6. App and algorithm development
  7. Big data analytics and predictive analytics
  8. Data acquisition and measurement

Pricing

  • Subscription-Based
  • Pay-As-You-Go

Pros

Powerful built-in math and graphics functions

Wide range of toolboxes for domain-specific tasks

Interoperability with C/C++, Java, Python, and other languages

Can handle large data sets and computations efficiently

Extensive visualization and debugging capabilities

Large user community and available resources

Cons

Expensive licensing model

Steep learning curve for new users

Not inherently object-oriented

Not open source

Platform dependent and not very portable

Code can be slower than compiled languages


Enthought

Enthought

Enthought is a Python-centered software company that provides tools and solutions for scientific computing, data analytics, and machine learning. Their flagship product is the Enthought Deployment Manager, which allows deployment of Python environments across an organization.

Categories:
python data-science analytics deployment

Enthought Features

  1. Enthought Deployment Manager for deploying Python environments
  2. Canopy Python distribution with scientific and analytic packages
  3. Training and support services for Python and data science
  4. Platform for building and deploying analytics web applications

Pricing

  • Subscription-Based
  • Pay-As-You-Go

Pros

Eases Python environment management and deployment

Comes with many pre-installed scientific and data science packages

Good technical support available

Integrated web framework for building analytics apps

Cons

Expensive licensing costs

Limited free offering compared to open source options

Less flexibility than rolling your own Python environment

Web framework not as full-featured as Django or Flask