JupyterLab
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include, data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala.
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Visual LANSA
Visual LANSA is a low-code development platform used by IT pros to build enterprise web and mobile apps faster, easier, and at a lower cost than traditional coding. Trusted by thousands of customers across the globe, Visual LANSA accelerates application development by eliminating the need to master multiple technical skills normally required to produce software applications. Once an app is developed, it can be deployed to an IBM i, Windows, or Linux server. One IDE, one language, no limitations.
Visual LANSA includes features such as access Controls/Permissions, code assistance, code refactoring, collaboration tools, compatibility testing, data modeling, debugging, deployment management, graphical user interface, mobile development, No-Code, Reporting/Analytics, software development, source control, and version control. Visual LANSA offers 24/7 live support and online support.
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Posit
Posit builds tools that help data scientists work more efficiently, collaborate seamlessly, and share insights securely across their organizations. Its Positron code editor provides the speed of an interactive console combined with the power to build, debug, and deploy data-science workflows in Python and R. Posit’s platform enables teams to scale open-source data science, offering enterprise-ready capabilities for publishing, sharing, and operationalizing applications. Companies rely on Posit’s secure infrastructure to host Shiny apps, dashboards, APIs, and analytical reports with confidence. Whether using open-source packages or cloud-based solutions, Posit supports reproducible, high-quality work at every stage of the data lifecycle. Trusted by millions of users—and more than half of the Fortune 100—Posit empowers professionals across industries to innovate with data.
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Julia
Julia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM. Julia uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns. The talk on the Unreasonable Effectiveness of Multiple Dispatch explains why it works so well. Julia is dynamically typed, feels like a scripting language, and has good support for interactive use. Julia provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager, and more. One can build entire Applications and Microservices in Julia. Julia is an open source project with over 1,000 contributors. It is made available under the MIT license.
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