DATA MINING AND KNOWLEDGE ACQUISITON
- Course
- MISY641 - DATA MINING AND KNOWLEDGE ACQUISITON
- Department
- Management Information Systems - English - PhD
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 8
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
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
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Course outcomes
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Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction |
| Week 2 | An Overview of Business Intelligence |
| Week 3 | Data Warehousing |
| Week 4 | Business Reporting, Visual Analytics, and Business, Performance Management |
| Week 5 | Introduction to Data Mining |
| Week 6 | Data Mining for Business Intelligence |
| Week 7 | Data in Data Mining |
| Week 8 | Midterm Exams |
| Week 9 | Midterm Exams |
| Week 10 | Project Presentations |
| Week 11 | Basic Data Classification |
| Week 12 | Cluster Analysis: Basic Concepts and Algorithms |
| Week 13 | Data Mining Processes |
| Week 14 | Data Mining Applications |
| Week 15 | Project Presentations |
Reference Books & Course Materials
- 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
- 02 Business Intelligence: A Managerial Perspective on Analytics, 3/E, Ramesh Sharda, Dursun Delen, Efraim Turban
- 03 Data Mining Techniques and Applications , 1st Ed., Hongbo Du, Cengage Learning
- 04 Introduction to Data Mining: Pearson New International Edition, 1st Ed., Pang-Ning Tan; Michael Steinbach; Vipin Kumar, Pearson.
Learning Outcomes
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Program Outcomes
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