| COMPUTER ENGINEERING (ENGLISH, NONTHESIS) | |||||
| 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 | 8 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| 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 |
| 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: | Yanchang Zhao ,R and Data Mining: Examples and Case Studies, Academic Press, Elsevier, 2012 |
| References: | none. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 0 | % 0 |
| Laboratory | 0 | % 0 |
| Application | 0 | % 0 |
| Field Work | 0 | % 0 |
| Special Course Internship (Work Placement) | 0 | % 0 |
| Quizzes | 0 | % 0 |
| Homework Assignments | 0 | % 0 |
| Presentation | 0 | % 0 |
| Project | 1 | % 30 |
| Seminar | 0 | % 0 |
| Midterms | 1 | % 30 |
| Preliminary Jury | 0 | % 0 |
| Final | 1 | % 40 |
| Paper Submission | 0 | % 0 |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 30 | |
| PERCENTAGE OF FINAL WORK | % 70 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 42 | |
| Presentations / Seminar | |||
| Project | 1 | 30 | |
| Homework Assignments | |||
| Quizzes | |||
| Preliminary Jury | |||
| Midterms | 1 | 30 | |
| Paper Submission | |||
| Jury | |||
| Final | 1 | 50 | |
| Total Workload | 194 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |