CMP5102 Data Mining IIBahçeşehir UniversityDegree Programs ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, THESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
ELECTRIC-ELECTRONIC ENGINEERING (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
CMP5102 Data Mining II Fall
Spring
3 0 3 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
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
Recommended Optional Program Components: none.
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.

Learning Outcomes

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.

Course Content

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

Weekly Detailed Course Contents

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

Sources

Course Notes / Textbooks: Yanchang Zhao ,R and Data Mining: Examples and Case Studies, Academic Press, Elsevier, 2012
References: none.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Project 1 % 30
Midterms 1 % 30
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 30
PERCENTAGE OF FINAL WORK % 70
Total % 100

ECTS / Workload Table

Activities Number of Activities Workload
Course Hours 14 42
Study Hours Out of Class 14 42
Project 1 30
Midterms 1 30
Final 1 50
Total Workload 194

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution