Research Article
Comparative Analysis and Dashboard Creation for Material Science
Md Wasim Rana*,
Data Science
Issue:
Volume 12, Issue 1, March 2026
Pages:
1-14
Received:
2 September 2025
Accepted:
30 September 2025
Published:
25 February 2026
Abstract: To fully visualise the intricate interactions among the mechanical, chemical and physical characteristics of materials sharing the same chemical formula, this project employs advanced data analytics in conjunction with a Power BI dashboard. Although these materials possess identical chemical compositions, variations in structural arrangements and manufacturing processes generate significant differences in their measurable properties. Such distinctions directly influence their functional performance and industrial applicability. This research systematically analyses variations in key attributes, including density, volume and band gap energy, through the application of advanced data mining algorithms. These computational methods are integrated into an interactive Power BI dashboard designed to facilitate intuitive and dynamic data exploration. By combining statistical modelling with visual analytics, the study establishes a structured comparative framework that reveals relationships between processing techniques, structural configurations and resulting performance outcomes. The findings indicate that even minor alterations in structural arrangement can produce substantial changes in material behaviour. Variations in band gap energy, for example, significantly affect electronic and optical properties, which are fundamental to semiconductor technologies. Similarly, differences in density and volume influence mechanical strength, thermal stability and durability, all of which are critical considerations in aerospace and automotive engineering. These results emphasise the importance of adopting a targeted, evidence-based approach to material selection and optimisation. Beyond functioning as a visual representation tool, the Power BI dashboard operates as a practical decision-support system. It enables dynamic filtering, real-time comparison and cross-sectional analysis, allowing researchers and industry professionals to derive insights tailored to specific technical requirements. This interactive capability enhances analytical efficiency while transforming complex datasets into accessible, actionable knowledge. By converting raw scientific data into structured visual intelligence, the project bridges the gap between theoretical material science and real-world engineering application. The integration of analytics and dashboard technology accelerates the identification of optimal material configurations, reduces experimental inefficiencies and supports informed strategic decision-making. Ultimately, this research contributes to the advancement of material science by demonstrating how data-driven methodologies can expand academic understanding, strengthen industrial innovation and improve performance outcomes across high-precision technological sectors.
Abstract: To fully visualise the intricate interactions among the mechanical, chemical and physical characteristics of materials sharing the same chemical formula, this project employs advanced data analytics in conjunction with a Power BI dashboard. Although these materials possess identical chemical compositions, variations in structural arrangements and m...
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Review Article
Data Science for Social Impact: Innovative Approaches to Solve Community Problems
Shaik Himam Saheb*
Issue:
Volume 12, Issue 1, March 2026
Pages:
15-23
Received:
15 April 2026
Accepted:
24 April 2026
Published:
22 July 2026
DOI:
10.11648/j.ijdst.20261201.12
Downloads:
Views:
Abstract: Data Science for Social Good is an emerging field that harnesses data-driven techniques to address complex societal challenges and promote positive change. This chapter presents a comprehensive study on the application of data science for social good, with a specific focus on addressing food insecurity in urban areas. By leveraging spatial analysis, predictive modelling, and cluster analysis, this study provides valuable insights into the extent and causes of food insecurity, identifies at-risk populations, and proposes evidence-based interventions. Collaboration between data scientists, policymakers, and community stakeholders is emphasized to develop effective strategies. Ethical considerations, including privacy protection and transparency, are prioritized to ensure responsible data use. The scalability of data science approaches, emerging trends, and future research directions are discussed. This chapter contributes to the growing body of knowledge on using data science to address social challenges and promote social good. The integration of real-time data, machine learning advancements, and community-driven insights enhances the effectiveness of interventions and supports sustainable, long-term solutions for reducing urban food insecurity.
Abstract: Data Science for Social Good is an emerging field that harnesses data-driven techniques to address complex societal challenges and promote positive change. This chapter presents a comprehensive study on the application of data science for social good, with a specific focus on addressing food insecurity in urban areas. By leveraging spatial analysis...
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