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Description

Eclipse Dirigible™ is an application platform designed for high productivity, offering both development tools and a runtime environment. It facilitates the complete development lifecycle of applications by utilizing an in-system programming model alongside rapid application development methods. This platform encompasses the entire development process, including database management and modeling, the creation of RESTful services with various dynamic languages, and user interface generation based on established patterns, as well as role-based security, integration of external services, testing, debugging, operations, and monitoring. All source code and example applications from the Eclipse Dirigible project are available under the Eclipse Public License v 2.0 and can be found on GitHub. Students can utilize this platform to work on projects, explore different technologies and scenarios, and learn widely-used programming languages. With Eclipse Dirigible, you have all the essential tools and resources at your disposal for any development venture you embark upon. Its comprehensive features make it an ideal choice for both beginners and experienced developers alike.

Description

Project Jupyter is dedicated to the creation of open-source tools, standards, and services that facilitate interactive computing in numerous programming languages. At the heart of this initiative is JupyterLab, a web-based interactive development environment designed for Jupyter notebooks, coding, and data manipulation. JupyterLab offers remarkable flexibility, allowing users to customize and organize the interface to cater to various workflows in fields such as data science, scientific research, and machine learning. Its extensibility and modular nature enable developers to create plugins that introduce new features and seamlessly integrate with existing components. The Jupyter Notebook serves as an open-source web application enabling users to produce and share documents that incorporate live code, mathematical equations, visualizations, and descriptive text. Common applications of Jupyter include data cleaning and transformation, numerical simulations, statistical analysis, data visualization, and machine learning, among others. Supporting over 40 programming languages—including popular ones like Python, R, Julia, and Scala—Jupyter continues to be a valuable resource for researchers and developers alike, fostering collaborative and innovative approaches to computing challenges.