Future days of the energy transition will add more responsibilities for data scientists such as battery technology, long-distance energy exchanges, investment optimization, and cost reduction. This highlights the significance of big data in optimizing and transforming renewable energy into a future energy source. Data science will be in high demand for sustainable energy soon, and more data scientists will be needed for the optimization of limited resources. Despite the mounting demand for data-related skills, the integration of data science and energy education seems to be overlooked even in well-reputed universities around the world. Consequently, graduates from energy engineering programs joining the workforce today have large gaps in their data literacy, and as such are often required to either learn on the job, or make use of outdated programming languages, tools, and skills. Domain integrated energy education can play a key role at the intersection of data science, artificial intelligence, and the energy industry. This paper proposes a web analytic tool to tackle the needs. The web framework includes integration of collected datasets, functioning of the data science lifecycle, and, insights formation. The proposed software integrates open-sourced datasets available from the public, private, government, non-government, and educational repositories (United Nations, DOE, Mendeley, Duke University Energy Initiative, National Geothermal Data System, National Renewable Energy Laboratory, USGS, Open Power System Data, World Bank, Duke Energy Initiative, National Geothermal Data System). The researchers and learners can experiment and, test out the end-to-end data science lifecycle on the platform. The major aspects of the domain viz. data cleaning, feature engineering, feature selection, model training, model evaluation, and business translation are integrated into the platform. It has three key segments: 1. Learning Resources; 2. Application Monitoring; 3. Results Summarization. The business and policy insights summarized on the platform are key connecting points to understanding the energy market using data. Major decisions such as energy deployment, resource allocation, land usage, technological inclusion, support forecast, and, energy demands; form the final segment of the modelling playground. This enhances the contextual problem-solving approach and equips energy enthusiasts with the necessary data skills for the future.