Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA ANALYTICS 805100715111 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Elective
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals The aim of this course is to grasp the methods which are used to facilitate extraction of meaningful data for decision making through very large amounts of data.
Course Content Data Mining Introduction to methods, data mining, pipeline, data pre-processing and cleaning, data reduction, data mining, basic items, cluster analysis, clustering, association rules, time series analysis and sequence mining, graph mining, data visualization and data warehousing. The detailed applications to different areas.
Learning Outcomes 1) Develops algorithms that make meaningful, usable inferences from large data.
2) Gives decision by making comments on the extracted data.
3) Able to analyze data

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Introdcution Lecture
Brainstorming
Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
2. Week Data processing Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
3. Week Similarities and distance metrics Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
4. Week Relation and pattern mining Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
5. Week Clustering analysis Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
6. Week Outlier analysis Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
7. Week Classification Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
8. Week Data stream mining Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
9. Week Text mining Lecture
Brainstorming
Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
10. Week Time series analysis Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
11. Week Mining of discrete data Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
12. Week Spatial data analysis Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
13. Week Graph data analysis Discussion

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
14. Week Web data mining Discussion

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)

Sources Used in This Course
Recommended Sources
Data Mining: The Textbook (Hardcover) by Charu C. Aggarwal

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15400
PY25050
PY45005

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 3
Homework 14 3
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 20
Final Exam 1 1
Time to prepare for Final Exam 1 50
14 3
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload