DATA MINING AND BUSINESS INTELLIGENCE
- Course
- ITEC542 - DATA MINING AND BUSINESS INTELLIGENCE
- Department
- Information Technologies - English - Master
- 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 students with areas, such as data warehouses and business intelligence technologies. This course also helps business managers to understand and provide a business strategy that is primarily concerned with the awareness of what these technologies are currently capable of and the creation of business intelligence for developers. This course provides a typical lifecycle of a data warehouse/business intelligence project, involving the following broad phases: extraction, transformation, and loading of data from source systems, particularly data analysts and building OLAP cubes. One of the focuses of this course is OLAP, which is rapidly becoming a ubiquitous technology that uses data mining and is one of the Fortune 100 companies’ technological tools available for business intelligence.
DATA MINING AND BUSINESS INTELLIGENCE
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
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
No learning outcomes have been defined.
Program Outcomes
No program outcomes have been defined.
Po-Lo Matrix
The PO-LO matrix has not been populated yet.