CYBER SECURITY (ENGLISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP5101 Data Mining Spring 3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

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 provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth

Learning Outputs

The students who have succeeded in this course;
I. Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase.
II. Be proficient with leading data mining software, including WEKA
III. Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor
IV. Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues.
V. Understand the mathematical statistics foundations of the algorithms outlined above
VI. Evaluate current research and advanced topics in data mining.

Course Content

1. Frequent Item Set Detection
2. Association Rule Mining
3. Clustering
4. Classification

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to Data Mining None
2) Frequent Item Set Mining None
3) Various frequent item set algorithms: Apriori, FPGrowth None
4) Association Rule Mining None
5) Classification None
6) Bayesian classification None
7) Midterm Exam 1 Review all the topics
8) Rule based classification None
9) Cluster Analysis None
10) k-means None
11) k-medoids None
12) Hierarchical clustering None
13) Cluster quality None
14) Combining Multiple Clusterings None

Sources

Course Notes: Data Mining Concepts and Techniques Jiawei Han and Micheline Kamber Morgan Kaufman
References:

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project 5 % 10
Seminar % 0
Midterms 1 % 40
Preliminary Jury % 0
Final 1 % 50
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 40
PERCENTAGE OF FINAL WORK % 60
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Laboratory
Application
Special Course Internship (Work Placement)
Field Work
Study Hours Out of Class 14 56
Presentations / Seminar
Project 16 48
Homework Assignments
Quizzes
Preliminary Jury
Midterms 3 15
Paper Submission
Jury
Final 7 35
Total Workload 196

Contribution of Learning Outcomes to Programme Outcomes

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.
1) 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.
1) Being able to independently carry out a work that requires expertise in the field.
1) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
1) 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.
2) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines,
2) 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.
2) 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.
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
2) 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.
2) To be able to comprehend the interdisciplinary interaction with which the field is related.
3) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
3) Being able to lead in environments that require the resolution of problems related to the field.
3) To be able to solve the problems encountered in the field by using research methods.