This module aims to cover methodology, major software tools and applications in data mining. Providing students with the foundational techniques and tools to extract valuable insights from databases (KDD). This module will equip students with both theoretical knowledge and practical skills in data mining algorithms, such as classification, clustering, association. By mastering these techniques, students will be able to analyze complex datasets, identify meaningful patterns, and apply these insights to various domains such as finance, healthcare, marketing, and social sciences. The course also focuses on handling real-world challenges such as data noise, scalability, and interpretability, preparing students to tackle practical data science problems effectively.
A. Demonstrate a foundational understanding of the core concepts and techniques of data mining, including data preprocessing, pattern discovery, clustering, classification, and association rule mining. B. Apply data analysis techniques that support decision-making processes, such as descriptive analytics, predictive modeling, and data visualization in various domains. C. Apply the principles of data mining to analyze large-scale problems, effectively handling big data challenges and extracting meaningful patterns and insights to inform strategic decisions. D. Use advanced data mining algorithms and tools, such as decision trees, neural networks, and ensemble methods, to solve practical problems.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments. The module will be delivered in a combination of lectures, seminars and labs. Lectures will introduce students to the academic content. Seminars and labs will be used to expand the students understanding of lecture materials. In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading. This module will utilize open-source artificial intelligence projects in conjunction with course content to help students achieve better learning outcomes. By integrating advanced AI technologies, we aim to improve the efficiency of teaching and interaction, while fostering greater student autonomy and flexibility in learning.