CS301 Data Structures

CS301: Data Structures

CS301 Data Structures is a critical, hands-on programming course that teaches you how to store, organize, and manage data efficiently. While a simple variable can hold a single piece of data, most real-world applications require handling large and complex collections of data. This course introduces a variety of fundamental data structures and explores the trade-offs—in terms of time and space—associated with each.

This course is the practical counterpart to the theoretical Design and Analysis of Algorithms. You will not only learn the concepts behind data structures like lists, stacks, queues, trees, and graphs, but you will also implement them from scratch. This process builds a deep understanding of how they work under the hood. You will learn how the choice of data structure can dramatically impact an application's performance.

Key Topics Covered:

• Abstract Data Types (ADTs): Understanding the concept of an ADT as a theoretical model, separate from its implementation.
• Linear Data Structures:
  • Arrays: Their static nature, advantages (fast access), and disadvantages (fixed size).
  • Linked Lists: Singly, doubly, and circular linked lists. Understanding dynamic memory allocation, insertion, and deletion.
  • Stacks: A Last-In, First-Out (LIFO) structure. Used in function calls, expression evaluation, etc.
  • Queues: A First-In, First-Out (FIFO) structure. Used in simulations, scheduling, and breadth-first search.
• Non-Linear Data Structures:
  • Trees: Hierarchical structures, including binary trees, binary search trees (BSTs), and their operations (insertion, deletion, traversal—in-order, pre-order, post-order).
  • Heaps: A specialized tree-based structure (Min-Heap, Max-Heap) used for implementing priority queues.
  • Graphs: Representing relationships between objects. Learning graph representations (adjacency matrix, adjacency list) and basic traversal algorithms (Breadth-First Search, Depth-First Search).
• Sorting and Searching:
  • Searching: Linear search and the much faster binary search (which requires sorted data).
  • Sorting: Simple sorting algorithms (Bubble Sort, Insertion Sort) and more efficient, recursive algorithms (Merge Sort, Quick Sort).
• Hash Tables: A powerful structure that provides average-case O(1) time for insertion, deletion, and search. Understanding hash functions and collision handling techniques.

Course Objectives:

1. Implement fundamental linear and non-linear data structures from scratch.
2. Analyze the time and space complexity of different data structures and their operations.
3. Select the most appropriate data structure to solve a given programming problem.
4. Gain proficiency in using pointers, dynamic memory, and recursion.
5. Build a strong foundation for advanced courses in algorithms, databases, and operating systems.

Mastering data structures is arguably one of the most important skills for a software engineer. This course provides the essential toolkit for building efficient, scalable, and robust software.