Data Mining Curriculum – A Proposal for a Comprehensive and Industry-Oriented Academic Framework

Abstract

The rapid growth of data across scientific, industrial, and social domains has created an increasing demand for professionals skilled in data mining and knowledge discovery. This proposal presents a structured and comprehensive data mining curriculum designed to equip students with both theoretical foundations and practical competencies required in modern data-driven environments. The proposed curriculum emphasizes core concepts such as data preprocessing, exploratory data analysis, classification, clustering, association rule mining, anomaly detection, and predictive modeling. In addition, it integrates essential topics including machine learning fundamentals, statistical analysis, big data technologies, data visualization, and ethical considerations in data usage. A strong focus is placed on hands-on learning through laboratory sessions, real-world datasets, case studies, and project-based assessments to bridge the gap between academic learning and industry requirements. The curriculum is designed to be modular and flexible, allowing adaptation across undergraduate and postgraduate programs while remaining aligned with evolving technological trends. Tools and platforms such as Python, R, SQL, and open-source data mining frameworks are incorporated to enhance practical exposure. Furthermore, the proposal highlights the importance of interdisciplinary learning by encouraging applications of data mining in healthcare, finance, business intelligence, cybersecurity, and scientific research. By fostering analytical thinking, problem-solving skills, and ethical awareness, this curriculum aims to prepare graduates for careers in data science, artificial intelligence, and advanced analytics, while also supporting research and innovation in the field of data mining.