In addition to the business intelligence (BI) component that provides visualizations of simulations and multi-dimensional analysis, the Cosmo Tech simulation can also be integrated with advanced data science libraries including machine learning, scientific computing, optimization, and design of experiments. This integration is particularly achieved by utilizing Spark computing libraries. Users have the ability to create simulation-based experiments by developing their own Python analytics. Data scientists can utilize well-known frameworks such as JupyterLab and PySpark libraries to implement algorithms like uncertainty and sensitivity analysis, parameter optimization with constraints, and pre/post-processing analysis. These algorithms seamlessly utilize our simulation engine through Spark.