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 | Fall | 3 | 0 | 3 | 7 |
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 | |
1) | Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications. | |
2) | To be able to comprehend the interdisciplinary interaction with which the field is related. | |
3) | To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field. | |
4) | To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines. | |
5) | To be able to solve the problems encountered in the field by using research methods. | |
6) | To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data. | |
7) | To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary. | |
8) | To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning. | |
9) | To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field. | |
10) | To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes. | |
11) | To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies. | |
12) | Being able to independently carry out a work that requires expertise in the field. | |
13) | To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility. | |
14) | Being able to lead in environments that require the resolution of problems related to the field. |