| 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 |
| CMP5102 | Data Mining II | Spring | 3 | 0 | 3 | 12 |
| 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 introduces some advanced and popular data mining topics with practical implementations. Applications shall be made on the R open-source software program. Basic information will be taught to use R programming language. |
|
The students who have succeeded in this course; I. To introduce advanced topics in data mining. II. Data mining methods and advanced programming tools that are used in various engineering fields and to use their skills to practice. III. And gain the ability to explore data relationships. IV. For problems involving data provide the ability to perform hypothesis testing. |
| Introduction, Data import and export, Data exploration, Decision trees and random forest, Network estimation, Outlier detection, Time serious analysis, Association rules, Text mining, Social network analysis, web mining, Case study I: Analysis and forecasting of house price indices, Case study II: Predictive modelling of Big Data with limited memory |
| Week | Subject | Related Preparation |
| 1) | Introduction | |
| 2) | Data import and export | |
| 3) | Data exploration | |
| 4) | Decision trees and random forest | |
| 5) | Network estimation | |
| 6) | Outlier detection | |
| 7) | Time serious analysis | |
| 8) | Association rules | |
| 9) | Text mining | |
| 10) | Social network analysis | |
| 11) | web mining | |
| 12) | Case study I: Analysis and forecasting of house price indices | |
| 13) | Vaka çalışması II: sınırlı bellek ile Büyük Veri Akıllı modelleme | |
| 14) | Projects |
| Course Notes / Textbooks: | Yanchang Zhao ,R and Data Mining: Examples and Case Studies, Academic Press, Elsevier, 2012 |
| References: | none. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Project | 1 | % 30 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 30 | |
| PERCENTAGE OF FINAL WORK | % 70 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 42 |
| Study Hours Out of Class | 14 | 42 |
| Project | 1 | 30 |
| Midterms | 1 | 30 |
| Final | 1 | 50 |
| Total Workload | 194 | |
| 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. | 3 |
| 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. | |
| 8) | Students will be able to measure and evaluate learners' performances in educational environments. | 1 |
| 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 |