Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA STRUCTURES 805101725061 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 Nuri ÖZALP
Assistants
Goals Aim of this course is to teach the students analysing and processing data structures
Course Content Week 1 Algorithm analysis Week 2 sorting Week 3 recursion-tree method Week 4 sorting algorithms Week 5 sorting in liner time Week 6 order statistics Week 7 elementary data structures Week 8 hash tables Week 9 binary search tree Week 10 red-black trees Week 11 augmenting data structures Week 12 greedy algorithms Week 13 amortized analysis Week 14 shortest paths
Learning Outcomes 1) Students will learn about qualitative and quantative data structures
2) Students will learn the optimum processing of data
3) ability of developing and comparing algorithms

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Algorithm analysis Lecture
Brainstorming
Project Based Learning 		Homework
2. Week sorting Lecture
Brainstorming
Project Based Learning 		Homework
3. Week recursion-tree method Lecture
Brainstorming
Project Based Learning 		Homework
4. Week sorting algorithms Lecture
Six Hats Thinking
Project Based Learning 		Homework
5. Week sorting in linear time Lecture
Brainstorming
Project Based Learning 		Homework
6. Week order statistics Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
7. Week elementary data structures Lecture
Brainstorming
Project Based Learning 		Homework
8. Week hash tables Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
9. Week binary search tree Lecture; Question Answer
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
10. Week red-black trees Lecture
Six Hats Thinking
Project Based Learning 		Homework
11. Week augmenting data structures Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
12. Week greedy algorithms Lecture
Brainstorming
Project Based Learning 		Presentation (Including Preparation Time)
13. Week amortized analysis Lecture; Question Answer
Six Hats Thinking
Problem Based Learning 		Presentation (Including Preparation Time)
14. Week shortest paths Lecture
Six Hats Thinking
Problem Based Learning 		Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
1. Cormen, Thomas H., Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, Introduction to Algorithms. 2nd ed. Cambridge, MA: MIT Press 2. Khurana, R. (2011). Data and File Structure. USA: VIKAS PUBLISHING HOUSE PVT LTD. 3. Hurwitz, Nugent, A., Halper, F. & Kaufman, M. (2013). Big Data For Dummies. USA: John Wiley & Sons, Inc. 4. Aggarwal, C. C. (2014). Data Classification: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series). Chapman and Hall/CRC. 5. Mehlhorn, K. & Sanders, P. (2010). Algorithms and Data Structures: The Basic Toolbox. Germany: Springer.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15000
PY13000
PY84000

*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 4
Homework 2 20
Midterm Exam 1 3
Time to prepare for Midterm Exam 1 30
Final Exam 1 3
Time to prepare for Final Exam 1 30
1 2
1 2
1 3
2 10
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