Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Utilize open-source machine learning tools and data visualization techniques to create dynamic data analysis workflows in a visual format, supported by a broad and varied collection of resources. Conduct straightforward data assessments accompanied by insightful visual representations, and investigate statistical distributions through box plots and scatter plots; for more complex inquiries, utilize decision trees, hierarchical clustering, heatmaps, multidimensional scaling, and linear projections. Even intricate multidimensional datasets can be effectively represented in 2D, particularly through smart attribute selection and ranking methods. Engage in interactive data exploration for swift qualitative analysis, enhanced by clear visual displays. The user-friendly graphic interface enables a focus on exploratory data analysis rather than programming, while intelligent defaults facilitate quick prototyping of data workflows. Simply position widgets on your canvas, link them together, import your datasets, and extract valuable insights! When it comes to teaching data mining concepts, we prefer to demonstrate rather than merely describe, and Orange excels in making this approach effective and engaging. The platform not only simplifies the process but also enriches the learning experience for users at all levels.
Description
The Vega-Altair open-source initiative operates independently from Altair Engineering, Inc. By utilizing Vega-Altair, users can focus more on grasping their data and its significance. Altair’s API is designed to be straightforward, user-friendly, and consistent, functioning atop the robust Vega-Lite visualization framework. This refined simplicity allows for the creation of stunning and impactful visualizations with minimal coding effort. The fundamental concept revolves around defining relationships between data columns and visual encoding channels, including the x-axis, y-axis, and color. Consequently, the intricate aspects of the plot are managed automatically. Expanding on this declarative plotting concept, a remarkable variety of both basic and advanced visualizations can be crafted using relatively succinct grammar, offering flexibility for different levels of data presentation. With its focus on ease of use, the Vega-Altair project empowers users to visualize complex data insights efficiently.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Python
No
Pricing Details
No price information available.
Free Trial
No
Free Version
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
University of Ljubljana
Country
Slovenia
Website
orange.biolab.si
Vendor Details
Company Name
Vega-Altair
Website
altair-viz.github.io
Product Features
Data Mining
Data Extraction
No
Data Visualization
No
Fraud Detection
No
Linked Data Management
No
Machine Learning
No
Predictive Modeling
No
Semantic Search
No
Statistical Analysis
No
Text Mining
No
Data Visualization
Analytics
No
Content Management
No
Dashboard Creation
Yes
Filtered Views
No
OLAP
No
Relational Display
No
Simulation Models
No
Visual Discovery
Yes
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No
Product Features
Data Visualization
Analytics
No
Content Management
No
Dashboard Creation
No
Filtered Views
No
OLAP
No
Relational Display
No
Simulation Models
No
Visual Discovery
No