Best Application Development Software for JupyterHub

Open-source web application, the Jupyter Notebook, allows you to create and share documents with live code, equations, and visualizations. Data cleaning and transformation, numerical modeling, statistical modeling and data visualization are just a few of the many uses.

Project Jupyter is an open-source project that develops open-standards software and services for interactive computing in dozens of programming languages. JupyterLab provides a web-based interactive environment for Jupyter notebooks and code. JupyterLab's user interface is flexible. You can configure and arrange it to support a variety of workflows in data science and scientific computing. JupyterLab can be extended and modified to add new components or integrate with existing ones. Open-source web application, Jupyter Notebook, allows you to create and share documents with live code, equations and visualizations. Data cleaning and transformation, numerical modeling, statistical modeling and data visualization are just a few of the many uses. Jupyter supports more than 40 programming languages, including Python and R, Julia, Scala, and Scala.

With a single keystroke, switch between editor and command modes. Use the arrow keys to navigate between cells. All the Jupyter shortcuts are available. Fully interactive outputs are available right under the cell. Editing code cells is easy with smart code completion, quick error checking and quick fixes, and easy navigation. You can connect to remote JupyterHub or JupyterLab servers from the IDE. Interactively run Python scripts and arbitrary expressions in a Python Console. You can see the outputs and the state variables in real time. Split Python scripts into code cells using the #%% separator, and run them individually in a Jupyter notebook. Interactive controls allow you to browse DataFrames or visualizations in real time. All popular Python scientific libraries, including Plotly and Altair, ipywidgets and others, are supported.

Understanding health issues requires the integration of complex and diverse data sources, even when they are within the context of single country interventions. Data can be gathered from HMIS platforms like DHIS2, individual tracking systems, custom software designed to address specific issues or various Excel reports provided in part by health professionals. It is often difficult to analyze and explore data from different sources. Collaboration is also difficult because many data analysts working with health data end up creating ad-hoc visualisations and scripts on their laptops. This makes it difficult for them to share their results in scattered publications, making it difficult to gain unified insights. Bluesquare created OpenHexa, which is a cloud-based data management platform that consists of three components: extraction, analysis, and visualization. This platform is largely based on open-source technologies that are mature.