Data Science – Analytics and Applications: Proceedings of the 3rd International Data Science Conference – iDSC2020

Data science is most revealing where research meets real-world decisions. These proceedings from iDSC 2020 bring together studies and practical contributions on how analytical methods can be developed, adapted, and put to work across business and industry.

Across topics including machine learning, AI, data mining, visualization, and applied analytics, the collection offers a varied view of the problems data science teams encounter—not only how models are built, but how they are used in organizational and industrial settings.

Research alongside practice

The conference proceedings bring together scientific papers and industry contributions. That mix gives readers multiple points of entry: methodological work sits alongside applications and case-based discussions, making it possible to compare research questions with the demands of deployment.

Methods grounded in real questions

Contributions address a broad range of problems, including Bayesian A/B testing for business decisions, outlier detection in bioinformatics, deep learning for 3D object classification, distributed stream learning for network security, and the automatic recognition of musical scores. Other work considers data mining methodology, optimization, and human migration as a complex network.

From analytical ideas to applications

Industry-facing material explores areas such as smart-meter service planning, data-driven tools for small and medium-sized enterprises, and computational environments for data science applications. The proceedings also discuss agile approaches to analytical business processes—a useful perspective for readers thinking about how evolving data projects fit into changing organizational needs.

For data science readers across disciplines

Researchers, students, and practitioners can use this collection to encounter varied applications and approaches within one volume. Its range makes it especially relevant to readers who want to see how data science connects with business decisions, industrial systems, and specialized technical challenges.