Audience
scientists, students
About DataMelt
DataMelt (or "DMelt") is an environment for numeric computation, data analysis, data mining, computational statistics, and data visualization. DataMelt can be used to plot functions and data in 2D and 3D, perform statistical tests, data mining, numeric computations, function minimization, linear algebra, solving systems of linear and differential equations. Linear, non-linear and symbolic regression are also available. Neural networks and various data-manipulation methods are integrated using Java API. Elements of symbolic computations using Octave/Matlab scripting are supported.
DataMelt is a computational environment for Java platform. It can be used with different programming languages on different operating systems. Unlike other statistical programs, it is not limited to a single programming language. This software combines the world's most-popular enterprise language, Java, with the most popular scripting language used in data science, such as Jython (Python), Groovy, JRuby.
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Pricing
Starting Price:
$0
Pricing Details:
Access to full documentation (examples, Java API documentation, online manual) requires one-time membership payment (20$/user)
Free Version:
Free Version available.
Company Information
jWork.ORG
Founded: 2005
United States
datamelt.org
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Product Details
Platforms Supported
Windows
Mac
Linux
Android
Training
Documentation
In Person
Support
Phone Support
DataMelt Frequently Asked Questions
DataMelt Product Features
Artificial Intelligence
For Healthcare
Image Recognition
Machine Learning
Natural Language Processing
Predictive Analytics
Chatbot
For Sales
For eCommerce
Multi-Language
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Data Analysis
Data Discovery
Data Visualization
Predictive Analytics
Regression Analysis
Statistical Modeling
Text Analytics
High Volume Processing
Sentiment Analysis
Data Mining
Data Extraction
Data Visualization
Machine Learning
Predictive Modeling
Statistical Analysis
Text Mining
Fraud Detection
Linked Data Management
Semantic Search
Data Visualization
Analytics
Simulation Models
Visual Discovery
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Deep Learning
Convolutional Neural Networks
Document Classification
ML Algorithm Library
Model Training
Self-Learning
Visualization
Image Segmentation
Neural Network Modeling
Statistical Analysis
Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Simulation
Survival Analysis
Time Series
Visualization
Statistical Process Control
