| EDUCATIONAL TECHNOLOGY (ENGLISH, THESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP5101 | Data Mining | Spring | 3 | 0 | 3 | 8 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
| Language of instruction: | English |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi TEVFİK AYTEKİN |
| Course Lecturer(s): |
Dr. Öğr. Üyesi TEVFİK AYTEKİN |
| Recommended Optional Program Components: | None |
| Course Objectives: | This course provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth |
|
The students who have succeeded in this course; I. Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase. II. Be proficient with leading data mining software, including WEKA III. Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor IV. Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues. V. Understand the mathematical statistics foundations of the algorithms outlined above VI. Evaluate current research and advanced topics in data mining. |
| 1. Frequent Item Set Detection 2. Association Rule Mining 3. Clustering 4. Classification |
| Week | Subject | Related Preparation |
| 1) | Introduction to Data Mining | None |
| 2) | Frequent Item Set Mining | None |
| 3) | Various frequent item set algorithms: Apriori, FPGrowth | None |
| 4) | Association Rule Mining | None |
| 5) | Classification | None |
| 6) | Bayesian classification | None |
| 7) | Midterm Exam 1 | Review all the topics |
| 8) | Rule based classification | None |
| 9) | Cluster Analysis | None |
| 10) | k-means | None |
| 11) | k-medoids | None |
| 12) | Hierarchical clustering | None |
| 13) | Cluster quality | None |
| 14) | Combining Multiple Clusterings | None |
| Course Notes / Textbooks: | Data Mining Concepts and Techniques Jiawei Han and Micheline Kamber Morgan Kaufman |
| References: |
| Semester Requirements | Number of Activities | Level of Contribution |
| Project | 5 | % 10 |
| Midterms | 1 | % 40 |
| Final | 1 | % 50 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 14 | 56 |
| Project | 16 | 48 |
| Midterms | 3 | 15 |
| Final | 7 | 35 |
| Total Workload | 196 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students will be able to demonstrate theoretical and practical knowledge in the areas of Educational/Instructional Technology. | 2 |
| 2) | Students will be able to conduct research in the area of Educational/Instructional Technology. | |
| 3) | Students will be able to plan and evaluate in the process of teaching information technologies. | |
| 4) | Students will be able to select and implement appropriate strategies and techniques for teaching information technologies. | |
| 5) | Students will be able to put their theoretical information into practice in the area of Educational/Instructional Technology. | |
| 6) | Students will be able to design and develop educational materials, software and games. | |
| 7) | Students will be able to implement information technologies effectively in and outside of educational environments. | 3 |
| 8) | Students will be able to measure and evaluate learners' performances in educational environments. | |
| 9) | Students will be able to self-improve their knowledge continuously in information technologies. | |
| 10) | Students will be able to act ethically in electronic and non-electronic educational environments, and pass these values to next generations. | 1 |
| 11) | Students will be able to plan, manage, and evaluate educational projects. | 1 |
| 12) | Students will be able to find out the technologic necessities of companies, and set up these technologies. | 1 |