Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA SCIENCE 805100715121 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 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 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
2. Week sorting Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
3. Week recursion-tree method Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
4. Week sorting algorithms Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
5. Week sorting in linear time Lecture

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

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

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
8. Week hash tables Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
9. Week binary search tree Lecture

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
10. Week red-black trees Lecture

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

Project Based Learning 		Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
12. Week greedy algorithms Lecture

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

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

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

Sources Used in This Course
Recommended Sources
Big Data For Dummies. USA: John Wiley & Sons, Inc. 4. Aggarwal, C. C. (2014).
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).
Data Classification: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series). Chapman and Hall/CRC. 5. Mehlhorn, K. & Sanders, P. (2010)

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3
PY15500
PY25040
PY85003

*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
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload