BBS654 - DATA WAREHOUSING and DATA MINING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| DATA WAREHOUSING and DATA MINING | BBS654 | Any Semester/Year | 3 | 0 | 3 | 6 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Drill and Practice Problem Solving Other: individual work | |||||
| Instructor (s) | Prof. Dr. Murat Caner TESTÄ°K | |||||
| Course objective | Develop skills to find patterns and regularities in massive data sets and extract useful knowledge from raw data | |||||
| Learning outcomes |
| |||||
| Course Content | ? Concepts of data mining ? Data preprocessing ? Principal components analysis ? Clustering ? Classification ? Prediction ? K-nearest neighbor algorithm ? Decision trees ? Artificial neural networks ? Association rules. | |||||
| References | ? Tan, P.N., Steinbach, M., Kumar, V. (2006). Introduction to Data Mining, Addison Wesley. ? Larose, D.T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining. Wiley Interscience. ? Shumeli, G., Patel, N.R., Bruce, P.C. (2012). Data Mining for Business Intelligence: Concepts, Techniques and Application in Microsoft Excel with XLMiner. E & B Plus. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to Data Mining |
| Week 2 | Types of Data/Data Quality |
| Week 3 | Data Preprocessing/Measures of Similarity |
| Week 4 | Exploring Data |
| Week 5 | Classification- Decision Trees |
| Week 6 | Classification- Decision Trees |
| Week 7 | Classification- Artificial Neural Network |
| Week 8 | Classification- Support Vector Machine |
| Week 9 | Midterm exam |
| Week 10 | Association Analysis |
| Week 11 | Multivariate Linear Regression |
| Week 12 | Cluster Analysis |
| Week 13 | Cluster Analysis |
| Week 14 | Project Presentations and Discussions |
| Week 15 | |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 1 | 30 |
| Seminar | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 2 | 60 |
| Percentage of final exam contributing grade succes | 1 | 40 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 12 | 4 | 48 |
| Presentation / Seminar Preparation | 1 | 5 | 5 |
| Project | 1 | 40 | 40 |
| Homework assignment | 0 | 0 | 0 |
| Midterms (Study duration) | 1 | 16 | 16 |
| Final Exam (Study duration) | 1 | 24 | 24 |
| Total Workload | 30 | 92 | 175 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Has detailed knowledge about Information Systems (IS). | X | ||||
| 2. Understands the interaction of theory and practice and the links between them. | X | ||||
| 3. Has a good understanding of common concepts such as abstraction, complexity, security, concurrency, software lifecycle and applies their expertise to the effective design, development and management of IS. | X | ||||
| 4. Has the ability to think at different levels of abstraction and detail; understands that an IS can be considered in different contexts, going beyond narrowly identifying implementation issues. | X | ||||
| 5. Solves any technical or scientific problem independently and presents the best possible solution; has the communication skills to clearly explain the completeness and assumptions of their solution. | X | ||||
| 6. Completes a project on a larger scale than an ordinary course project in order to acquire the skills necessary to work efficiently in a team. | X | ||||
| 7. Recognises that the field of informatics is rapidly evolving. Follows the latest developments, learns and develops skills throughout their career. | X | ||||
| 8. Recognises the social, legal, ethical and cultural issues related to informatics practice and conduct professional activities in accordance with these issues. | X | ||||
| 9. Can make oral presentations in English and Turkish to different audiences face-to-face, in writing or electronically. | X | ||||
| 10. Recognises that informatics has a wide range of applications and opportunities. | X | ||||
| 11. Is aware that informatics interacts with different fields, can communicate with experts from different fields and can learn necessary field knowledge from them. | X | ||||
| 12. Define a research problem and use scientific methods to solve it. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest