Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
6BİL314Data Mining3+0+035

Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Computer Engineering
Mode of Delivery Face to Face
Type of Course Unit Compulsory
Objectives of the Course With this course, students will learn the information discovery processes in databases, data mining concept, methods and frequently used data mining algorithms and apply these algorithms in simple level.
Course Content Data; information and knowledge concepts; Introduction to data mining; Knowledge discovery in databases (KDD); Databases; OLTP; Data warehouses; Data cubes; OLAP; KDD- data select; KDD- data preprocessing (data cleaning – data transformation); Classification concepts (decision trees; ID3 and bayes algorithms; etc.); Cluster concepts (kmeans; k-medoids; dbscan algorithms; etc.); Association rules concepts (market basket; apriori algorithm; etc.); Case study with apriori algorithm.
Course Methods and Techniques 1. Lecture, 2. Question and Answer, 3. Discussion and Brainstorming, 4. Research and Project Based Learning, 5. Presentation Preparation, 6. Group Work, 7. Problem Solving
Prerequisites and co-requisities None
Course Coordinator Asist Prof. Feridun C. ÖZÇAKIR feridun.ozcakir@gedik.edu.tr
Name of Lecturers Asist Prof.Dr. Feridun C. ÖZÇAKIR feridun.ozcakir@gedik.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources I. Witten – E. Frank; Data Mining, Morgan Kaufmann Academic Press; 2000
Mohammed .J. Zaki, Wagner Meira; Data Mining and Analysis Fundamental Concepts and Algorithms, Cambridge
Data Mining: Concepts and Techniques, 3rd Edition
Jiawei Han, Micheline Kamber, Jian Pei - Morgan Kaufmann - 2011
Vize ve Final sınavları

Course Category
Mathematics and Basic Sciences %40
Engineering %20
Engineering Design %10
Social Sciences %0
Education %0
Science %0
Health %0
Field %30

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 25
Project 1 % 10
Final examination 1 % 60
Total
3
% 95

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 3 1 3
Presentation 1 25 25
Mid-terms 1 2 2
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 5 116

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 To learn the basic concepts of Data Mining. (Learns the concepts of data, information, knowledge and knowledge discovery processes from databases)
2 To learn about Classification, Clustering, Association Rule, etc. Data Mining Methods and Algorithms. To applies them on data.
3 To learn a computer program on Data mining
4 To research different data mining methods and algorithms, create data analysis models specific to these algorithms, and apply these algorithms on the data.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Data, Databases, Data warehouses, Data models, OLTP and OLAP Related References Related References
2 E/R model, Relational model, Big data, New generation databases, Information and Knowledge concepts Related References Related References
3 Introduction to the concept of data mining and knowledge discovery in databases (KDD) processes. Data mining package applications (Knime, Anaconda - Orange, etc.) Related References Related References
4 Knowledge discovery in databases (KDD) processes: Data selection and Data preprocessing Related References Related References
5 Knowledge discovery in databases (KDD) processes in databases: Data reduction Related References Related References
6 Data mining methods: Classification (Decision trees, ID3) Related References Related References
7 Data mining methods: Classification (Bayesian, Naive Bayes) Related Sources Related Sources
8 Midterm Exam
9 Data mining methods: Clustering (AGNES, DIANA, K-Means, K-Medoids, DB-SCAN) Related Sources Related Sources
10 Data mining methods: Association-Rule (Support and Confidence values) Related References Related References
11 Data mining methods: Association-Rule (Market Basket) Related References Related References
12 Data mining methods: Association-Rule (Apriori Algorithm) Related References Related References
13 Student Presentations (Data Mining Algorithms) Related References Related References
14 Student Presentations (Data Mining Algorithms) Related References Related References
15 Student Presentations (Data Mining Algorithms) Related References Related References


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
C1 3 4 3 1
C2 3 3 3 3
C3 5
C4 3 5 3 4 5 3 2

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https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=205997&curProgID=5607&lang=en