DATA MINING AND KNOWLEDGE ACQUISITON

Course Description

This course provides an in-depth examination of data mining and knowledge acquisition within the broader scope of Business Intelligence (BI). It covers theoretical foundations, emerging research trends, and advanced methodologies for extracting meaningful insights from complex organizational data. Students will explore data warehousing, preprocessing, classification, clustering, and algorithmic analysis while critically evaluating BI and DM models from both technical and managerial perspectives. Emphasis is placed on applying BI frameworks to strategic decision-making and organizational performance. Through paper discussions, case studies, and project-based research, students will enhance their ability to design and implement advanced data mining workflows while developing scholarly communication and analytical reasoning skills.

DATA MINING AND KNOWLEDGE ACQUISITON

Evaluation Tools (Active Term)

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Course outcomes

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Course Syllabus

Reference Books & Course Materials

1. 01 Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2nd Edition, Galit Shmueli; Nitin R. Patel; Peter C. Bruce
2. 02 Business Intelligence: A Managerial Perspective on Analytics, 3/E, Ramesh Sharda, Dursun Delen, Efraim Turban
3. 03 Data Mining Techniques and Applications , 1st Ed., Hongbo Du, Cengage Learning
4. 04 Introduction to Data Mining: Pearson New International Edition, 1st Ed., Pang-Ning Tan; Michael Steinbach; Vipin Kumar, Pearson.

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

1. Demonstrate mastery of advanced research methodologies (quantitative, qualitative, and mixed methods) by critically analyzing literature, identifying research gaps, and designing original studies that contribute to MIS theory and practice.
2. Conduct and defend an original doctoral dissertation that reflects independent scholarly inquiry, academic rigor, and a significant contribution to the advancement of knowledge in MIS.
3. Exhibit readiness for thesis monitoring and defense by articulating the philosophical foundations of research paradigms, positioning one's research within these frameworks, and responding to scholarly critique.
4. Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
5. Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
6. Employ advanced data science techniques, including statistical modeling, machine learning, and AI-based analytics, to examine complex datasets and extract meaningful insights in MIS research.
7. Recognize and evaluate emerging technologies such as artificial intelligence, big data, blockchain, and the Internet of Things, assessing their transformative impact on organizational processes and digital ecosystems.
8. Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
9. Publish high-quality research in peer-reviewed journals, present findings at international conferences, and actively engage in academic service such as journal reviewing, conference organizing, and committee participation.
10. Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
11. Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
12. Develop advanced information systems and decision support systems that align IT capabilities with organizational strategies using systems thinking and innovative design methodologies.

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