Akademik Dersler - İçerik

Recalls fundamental concepts in data structure
Has a background of OOP.
Relates the algorithms and its corresponding mathematical complexity measurements.
Defines core OOP concepts in Java (class, object, inheritance, encapsulation, polymorphism) and states the differences among them.
Defines arrays, linked lists, stacks, queues, trees, hash tables, and graphs and lists their basic properties.
Defines the concept of pointers/references, memory addresses, and Java's reference semantics.
Explains and interprets concepts
EN: Explains how arrays are stored in memory, the indexing logic, and the advantages/disadvantages of fixed size.
EN: Explains LIFO (Stack) and FIFO (Queue) principles and justifies which structure to use in real-life scenarios.
EN: Explains the structure of a binary search tree (BST), traversal methods (inorder, preorder, postorder), and the imbalance problem.
Explains the purpose of a hash function, collision resolution strategies (chaining, open addressing), and the load factor.
Compares directed and undirected graphs, and explains adjacency matrix vs. adjacency list representations.
Uses knowledge in concrete situations
Designs and implements a multi-level class hierarchy in Java using inheritance and interfaces.
Implements linked list, stack, and queue data structures from scratch in Java using reference/pointer logic, supporting insert, delete, and search operations.
Inserts, deletes, and searches elements in a binary search tree and produces sorted output via inorder traversal.
Breaks down and compares components
Analyzes and compares the time complexity (Big-O) of insertion, deletion, and access operations for arrays, linked lists, stacks, and queues.
Analyzes best/average/worst-case complexities of balanced (AVL, Red-Black) and unbalanced BSTs.
Analyzes hash table performance with respect to load factor and collision strategy, explaining the conditions for O(1) amortized access.
Designs and produces something new
Designs and develops an original Java application that combines appropriate data structures to solve a real-world problem (e.g., task manager, social network analysis, autocomplete).
Writes a technical report documenting the time/space complexity of the chosen data structures and validates code correctness with unit tests.