Data Structure
Why?
Data Structures are foundational to efficient computing and algorithm design. Well-designed data structures enable faster data retrieval, efficient memory usage, and more elegant solutions to some complex problems. Although probably you won't be working on implementing them, but surely you will need to understand them to be able to dive into deeper concepts later. This course is typically considered hard because it invokes new way of thinking, but it boosts your problem-solving and programming skills.
What?
This course explores the design, implementation, and analysis of fundamental data structures. You will learn about arrays, linked lists, stacks, queues, trees, and various implementation strategies. The course also covers algorithm analysis techniques to evaluate and compare the efficiency of different data structures and algorithms for specific tasks.
Curriculum:
OOP Revision
Review of object-oriented programming concepts essential for implementing data structures, including classes, inheritance, polymorphism, and encapsulation.
Linked Lists
Implementing and working with singly and doubly linked lists, understanding pointers and references, insertion and deletion operations, and traversal techniques.
Queues and Array Implementation
Understanding queue data structures, FIFO principle, implementing queues using arrays, and handling queue operations efficiently.
Queues and Linked List Implementation
Implementing queues using linked lists, comparing with array-based implementations, and understanding the trade-offs between different implementation strategies.
Stacks and Array Implementation
Understanding stack data structures, LIFO principle, implementing stacks using arrays, and applications of stacks in algorithm design.
Stacks and Linked List Implementation
Implementing stacks using linked lists, comparing with array-based implementations, and understanding when to use each approach.
Binary Trees
Introduction to tree data structures, binary tree properties, traversal algorithms (in-order, pre-order, post-order), and applications of binary trees.
Recursive Methods
Understanding and implementing recursive algorithms, recursion in tree traversal, recursive vs. iterative approaches, and handling base cases.
Algorithm Analysis
Techniques for analyzing algorithm efficiency, Big O notation, time and space complexity, and comparing algorithms based on their performance characteristics.
Notes
You will learn typical data structures that exist everywhere. You can find the exact code you need everywhere, and the code you write in class may be exactly the same you must write in the exam. Nevertheless, Never memorize code. The course invokes you to learn how to think about algorithms rather than syntax, so focus on the ideas and you will automatically kinda memorize the code after implementing it by yourself.
