Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty of Science
  • Department of Mathematics
  • Course Structure Diagram with Credits
  • Applied Data Structures and Algorithms
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title MAT3501 - Applied Data Structures and Algorithms
Course Type Area Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 5.0
Course Instructor(s) DOÇENT DOKTOR UTKU ERDOĞAN
Mode of Delivery The mode of delivery of this course is Face to face
Prerequisites
Courses Recomended
Required or Recommended Resources
Recommended Reading List
Assessment methods and criteria Midterm I , Midterm II ,  Final exam
Work Placement N/A
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Introduction to Linear Data Structures and Basic Operations (Max/Min, Insertion, Deletion)
Week - 2 Basic Sorting Algorithms (Bubble, Selection, Insertion, Shell Sort) and Reversal
Week - 3 Linear Search and Binary Search Algorithms
Week - 4 Linked Lists I: Singly (Unidirectional) and Doubly Linked Lists
Week - 5 Linked Lists II: Circular Linked Lists, Stack and Queue Structures
Week - 6 Recursive Algorithms and Classical Problems (Fibonacci, Towers of Hanoi)
Week - 7 Recursive Algorithms and Classical Problems (Fibonacci, Towers of Hanoi)
Week - 8 Binary Search Trees (BST), Tree Traversal Methods and Node Operations
Week - 9 Binary Heap Sort and Hash Tables
Week - 10 Graph Theory, Depth-First Search (DFS) and Breadth-First Search (BFS)
Week - 11 Topological Sorting in Directed Graphs and Dijkstra’s Algorithm
Week - 12 State Space and Search Simulations: Rat in a Maze and Eight Coins Problem
Week - 13 Dynamic Programming and Optimization: Knapsack Problem
Week - 14 Circular Data Modeling: Josephus Problem

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Question & Answer
  • Observation
  • Team/Group Work
  • Drill - Practise
  • Competences
  • Productive
  • Rational
  • Questoning
  • Creative
  • Follow ethical and moral rules
  • Work in teams
  • Use time effectively
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Applying theoretical knowledge into practice
  • Information Management
  • To work autonomously
  • Organization and planning
  • Elementary computing skills
  • Decision making

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 25
Practical Work 1 25
Final Exam 1 50
Toplam (%) 100
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