Introduction to Data Structures PDF

Introduction to Data Structures Chapter 1 Overview What are Data Structures? A data structure is a method of organizing, managing, and storing data so it can be accessed and modified efficiently. They form the basis of creating algorithms that solve problems efficiently. Data structu...

Introduction to Data Structures Chapter 1 Overview What are Data Structures? A data structure is a method of organizing, managing, and storing data so it can be accessed and modified efficiently. They form the basis of creating algorithms that solve problems efficiently. Data structures are used to improve performance and manage complexity. Importance of Data Structures Efficiency in Data Management: Optimizes data storage and retrieval. Algorithm Optimization: Reduces time complexity (e.g., O(n) vs. O(log n)). Scalability: Handles increasing data efficiently (e.g., social media graphs). Memory Management and Data Integrity Memory Efficiency: Linked lists enable dynamic memory allocation. Data Integrity: Queues manage resources in multi-threaded environments. Types of Data Structures Primitive: Integer, Float, Boolean. Non-Primitive: Linear (Arrays, Linked Lists), Non-Linear (Trees, Graphs). Linear Data Structures Array: Efficient access, slow insertion. Linked List: Efficient for dynamic size, slow access. Stack: LIFO structure (Push, Pop). Queue: FIFO structure (Enqueue, Dequeue). Non-Linear Data Structures Tree: Hierarchical structure, used for efficient search (O(log n)). Graph: Represents networks (e.g., social media connections). Understanding Big O Notation Measures efficiency of operations. Examples: O(1): Constant time. O(n): Linear time. O(log n): Logarithmic time. O(n^2): Quadratic time. O(2^n): Exponential time. Operations on Data Structures Insertion: Adding an element. Deletion: Removing an element. Traversal: Accessing all elements. Searching: Finding an element. Sorting: Arranging elements in order. Applications of Data Structures Search Engines: Trees and graphs for organizing and searching web pages. Databases: B-trees for efficient record retrieval. Example: Queues for task scheduling, Stacks for expression evaluation. Social Networks, Operating Systems. Conclusion Choosing the right data structure is critical for efficient problem-solving.  Review of Basic Programming Concepts Structured and Object-Oriented Programming (Using C++ & Python) What is Structured Programming? A programming paradigm focused on control structures: Sequence (instructions executed one after the other), Selection (decision-making), and Iteration (repeats a block of code). Example of Sequence in C++ and Python Python Code: a = 5 b = 10 sum = a + b print(sum) # Outputs 15 C++ Code: int a = 5; int b = 10; int sum = a + b; std::cout b) { std::cout