Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA TRANSMISSION METHODS 805101725111 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 		Homework

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
PY15000
PY85000

*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
Project (Including Preparation and presentation Time) 2 10
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 2 10
Midterm Exam 1 3
Time to prepare for Midterm Exam 1 30
Final Exam 1 3
Time to prepare for Final Exam 1 30
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