Information Security Technology
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 08121106 | Data Mining | 2025 | Spring | 2 | 2+2+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | There are no prerequisites for the course. All students receive instruction starting from the basic level. |
| Coordinator | - |
| Instructor(s) | Lect. Ayşe Merve BÜYÜKBAŞ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Ayşe Merve BÜYÜKBAŞ | C-127 | [email protected] | 7436 | Wednesday 10:00-12:00 |
Course Content
This course includes the definition of data mining, an overview of data mining application areas, techniques and models, data mining stages: determining the purpose, creating a data set, data extraction and preprocessing, data reduction and data transformation, choosing the data mining learning algorithm, data mining learning algorithms, classification, curve fitting, correlation mapping, memory-based methods and k-neighbors algorithm.
Objectives of the Course
It lays out the fundamentals of data science, which is rapidly evolving in the 21st century and is very popular with researchers and practitioners alike. The course transfers the basic skills that a data scientist should have to the student and enables the student to apply them to different fields.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | X |
| Transferable Skills Courses | X |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | He/she has basic, current and practical knowledge about his/her profession. | 4 |
| P4 | Uses professionally relevant information technologies (software, programs, animations, etc.) effectively. | 4 |
| P5 | Has the ability to independently evaluate professional problems and issues with an analytical and critical approach and to propose solutions. | 2 |
| P9 | It has social, scientific, cultural and ethical values in the stages of collecting data related to its field, its application and the announcement of its results. | 5 |
| P20 | To enable students to gain the competence to solve the problems they encounter in their academic and professional lives by using information technologies effectively and efficiently. | 2 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Explains basic computer architecture. | P.1.4 | 7 |
| O2 | Knows how to develop algorithms and creates data structures appropriate to the algorithm. | P.4.3 | 1,2,7 |
| O3 | Generates appropriate solution alternatives. | P.5.3 | 2,7 |
| O4 | Follows ethical standards in data collection and analysis. | P.9.2 | 1,2,7 |
| O5 | Complies with legal and ethical responsibilities regarding data privacy, data security, and intellectual property rights during professional activities. | P.9.6 | 2,7 |
| O6 | Defines the fundamental concepts of information technologies and computer systems, and explains the relationships between these concepts. | P.20.1 | 1,3,5 |
| O7 | Effectively uses basic software applications (e.g., presentation software, etc.) and prepares professional documents using these tools. | P.20.2 | 4,6,7 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Definition of Data Mining |
| 2 | Overview of Data Mining Application Areas, Techniques, and Models |
| 3 | Veri madenciliği aşamaları: Amacı belirleme. |
| 4 | Creating a Dataset |
| 5 | Data Extraction and Preprocessing |
| 6 | Data Reduction and Data Transformation |
| 7 | Choosing a Data Mining Learning Algorithm |
| 8 | Midterm Exam |
| 9 | Data Mining Learning Algorithms |
| 10 | Classification |
| 11 | Curve Fitting |
| 12 | Correlationship Establishment |
| 13 | Memory Based Methods |
| 14 | K-Neighbors Algorithm |
| 15 | Final Exam |
Textbook or Material
| Resources | Notes shared by the course instructor |
| Data Mining Methods and R Applications, Assoc. Prof. Dr. Bülent ALTUNKAYNAK, Seçkin Publications, 2017. Silahtaroğlu, G., Data Mining, Papatya Publishing House, 2008. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | 1 | 5 (%) |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 35 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 8 | 112 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 1 | 14 | 14 |
| Midterms | 1 | 10 | 10 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 1 | 4 | 4 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 10 | 10 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P4 | P5 | P9 | P20 |
|---|---|---|---|---|---|---|
| O1 | Explains basic computer architecture. | 4 | 3 | 1 | 1 | 2 |
| O2 | Knows how to develop algorithms and creates data structures appropriate to the algorithm. | 3 | 4 | 3 | 1 | 3 |
| O3 | Generates appropriate solution alternatives. | 2 | 3 | 4 | 2 | 3 |
| O4 | Follows ethical standards in data collection and analysis. | 2 | 2 | 2 | 5 | 2 |
| O5 | Complies with legal and ethical responsibilities regarding data privacy, data security, and intellectual property rights during professional activities. | 2 | 2 | 2 | 5 | 2 |
| O6 | Defines the fundamental concepts of information technologies and computer systems, and explains the relationships between these concepts. | 4 | 3 | 1 | 1 | 3 |
| O7 | Effectively uses basic software applications (e.g., presentation software, etc.) and prepares professional documents using these tools. | 2 | 4 | 1 | 1 | 3 |