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 | 1 | 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 | Dersin herhangi bir ön koşulu bulunmamaktadır. Tüm öğrencilere temel seviyeden başlanarak eğitim verilmektedir. |
| Coordinator | - |
| Instructor(s) | Lect. Ayşe Merve BÜYÜKBAŞ |
| Instructor Assistant(s) | - |
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 | |
| Transferable Skills Courses | |
| 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 | P.1.4 | 7 | |
| O2 | P.4.3 | 1,2,7 | |
| O3 | P.5.3 | 2,7 | |
| O4 | P.9.2 | 1,2,7 | |
| O5 | P.9.6 | 2,7 | |
| O6 | P.20.1 | 1,3,5 | |
| O7 | 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 | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| 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 | Temel bilgisayar mimarisini açıklar. | - | - | - | - | - |
| O2 | Algoritma geliştirmeyi bilir ve algoritmaya uygun veri yapısı oluşturur. | - | - | - | - | - |
| O3 | Uygun çözüm alternatiflerini üretir. | - | - | - | - | - |
| O4 | Veri toplama ve analizinde etik standartları takip eder. | - | - | - | - | - |
| O5 | Mesleki faaliyetleri sırasında veri gizliliği, veri güvenliği ve fikri mülkiyet hakları konularındaki yasal ve etik sorumluluklara uyar. | - | - | - | - | - |
| O6 | Bilgi teknolojileri ve bilgisayar sistemlerinin temel kavramlarını tanımlayabilme ve bu kavramlar arasındaki ilişkileri açıklayabilme. | - | - | - | - | - |
| O7 | Temel yazılım uygulamalarını (örneğin sunum yazılımı vb.) etkin bir şekilde kullanabilme ve bu araçlarla profesyonel belgeler hazırlayabilme. | - | - | - | - | - |