Data Structures

1. Introduction

1. Data Types - Explanation of different types of data in programming.
2. Reference Type - Understanding objects stored by reference.
3. Value Type - How value types store data directly in memory.
4. Storing Data in Computer Memory - Overview of how data is managed in memory.

2. Array

1. Introduction and Basic Concepts - Fundamentals of arrays and their importance.
2. Static Array - Arrays with fixed size and their characteristics.
3. Dynamic Array - Resizable arrays and how they manage memory.
4. Array Operations - Common operations like insertion, deletion, searching, and sorting.
5. Application Areas of Arrays - Practical applications of arrays in real-world scenarios.

3. Singly Linked List

1. Introduction and Basic Concepts - Understanding singly linked lists and their significance in data structures.
2. Node Structure - Explanation of the SinglyLinkedListNode class, which stores data and a pointer to the next node.
3. Singly Linked List Operations
   • Insertion - Adding elements at the beginning, end, or a specific position.
   • Deletion - Removing the first, last, or a specific node.
   • Traversal - Iterating through the linked list using an enumerator.
   • Searching - Finding a specific node in the list.
4. Memory Management in Singly Linked Lists - How nodes are dynamically allocated and deallocated.
5. Application Areas of Singly Linked Lists - Practical uses in software development, such as queue implementations, undo features, and memory-efficient data storage.

4. Stack

1. Introduction and Basic Concepts

   • Understanding the LIFO (Last-In, First-Out) principle that defines a stack.
   • Real-world analogies like a stack of plates or books.
   • Importance in algorithm design and memory management.

2. Stack Structure

   • Explanation of the internal structure of a stack.
   • Common implementations using arrays and linked lists.
   • Core components: push, pop, peek, and isEmpty operations.

3. Stack Operations

   • Push - Adding an element to the top of the stack.
   • Pop - Removing the top element from the stack.
   • Peek (or Top) - Viewing the top element without removing it.
   • IsEmpty - Checking if the stack is empty.
   • Size - Getting the number of elements in the stack.

4. Memory Management in Stack

   • Stack memory allocation and deallocation.
   • Call stack usage during function execution.
   • Stack overflow issues and limitations in memory-constrained environments.

5. Application Areas of Stacks

   • Function call management in programming languages (call stack).
   • Expression evaluation and syntax parsing.
   • Backtracking algorithms (e.g., maze solving, undo operations).
   • Depth-First Search (DFS) in graph traversal.

5. Queue

1. Introduction and Basic Concepts

• Understanding the FIFO (First-In, First-Out) principle that defines a queue.
• Real-world analogies such as queues in supermarkets or print job scheduling.
• Importance in algorithm design, scheduling, and resource management.

2. Queue Structure

• Explanation of the internal structure of a queue.
• Common implementations using arrays, linked lists, and circular buffers.
• Core components: enqueue, dequeue, peek, and isEmpty operations.

3. Queue Operations

• Enqueue - Adding an element to the rear (end) of the queue.
• Dequeue - Removing an element from the front of the queue.
• Peek (or Front) - Viewing the front element without removing it.
• IsEmpty - Checking if the queue is empty.
• Size - Getting the number of elements currently stored in the queue.

4. Memory Management in Queue

• Dynamic vs. static memory allocation for queues.
• Circular queue implementations to optimize space utilization.
• Buffer overflow/underflow issues and preventive mechanisms.

5. Application Areas of Queues

• Scheduling processes in operating systems (CPU scheduling, IO queues).
• Managing print jobs in printers (print spooling).
• Breadth-First Search (BFS) in graph traversal.
• Simulation systems (e.g., customer service systems, traffic flow models).
• Message queues in distributed systems and asynchronous programming.

6. Sorting Algorithms

1. Introduction to Sorting Algorithms

• Importance of sorting in computer science.
• Comparison of different sorting techniques based on time complexity, space complexity, and stability.

2. Bubble Sort

• Simple comparison-based algorithm.
• Repeatedly swaps adjacent elements if they are in the wrong order.
• Best case: O(n), Worst case: O(n²), Space: O(1).

3. Insertion Sort

• Builds the final sorted array one element at a time.
• Good for small or nearly sorted datasets.
• Best case: O(n), Worst case: O(n²), Space: O(1).

4. Selection Sort

• Repeatedly finds the minimum element and moves it to the sorted portion.
• Always O(n²) time complexity.
• Not stable but simple to implement.

5. Merge Sort

• Divide-and-conquer algorithm that divides the list into halves, sorts them, and merges.
• Time complexity: O(n log n) in all cases.
• Space complexity: O(n).

6. Quick Sort

• Divide-and-conquer algorithm that picks a pivot and partitions the array.
• Best/Average case: O(n log n), Worst case: O(n²) (can be improved with randomized pivot).
• Space: O(log n) due to recursion stack.

7. Algorithm Analysis Techniques

1. Time Complexity

• Time complexity describes how the runtime of an algorithm increases with the size of the input.
• Helps evaluate the efficiency of algorithms.
• Common complexities:
  • O(1) – Constant time
  • O(log n) – Logarithmic time
  • O(n) – Linear time
  • O(n log n) – Linearithmic time
  • O(n²) – Quadratic time
  • O(2ⁿ), O(n!) – Exponential time

2. Asymptotic Notation

• Used to describe the upper, lower, or tight bound behavior of an algorithm:
  • Big O (O) – Worst-case upper bound
    Example: O(n²) means the runtime grows at most as n².
  • Big Omega (Ω) – Best-case lower bound
    Example: Ω(n) means the algorithm takes at least linear time.
  • Big Theta (Θ) – Tight bound (both upper and lower)
    Example: Θ(n log n) means the runtime grows exactly at that rate.

3. Substitution Method

• A method to solve recurrence relations by guessing the form of the solution and proving it using mathematical induction.
• Example:
  • T(n) = 2T(n/2) + n
  • Guess: T(n) = O(n log n)
  • Prove the guess using induction.

4. Iteration Method

• Solves recurrence relations by expanding them step by step until a pattern is observed.
• Example:
  • T(n) = T(n/2) + n
  • T(n/2) = T(n/4) + n/2
  • …
  • Total = n + n/2 + n/4 + … = O(n)

5. Master Theorem

• Provides a direct way to analyze the time complexity of divide-and-conquer algorithms.

• Applies to recurrences of the form:
  T(n) = aT(n/b) + f(n)

• Cases:

  1. If f(n) = O(n^log_b(a - ε)) → T(n) = Θ(n^log_b(a))
  2. If f(n) = Θ(n^log_b(a)) → T(n) = Θ(n^log_b(a) * log n)
  3. If f(n) = Ω(n^log_b(a + ε)) and regularity condition holds → T(n) = Θ(f(n))

• Example:

  • Merge Sort: T(n) = 2T(n/2) + n → Case 2 → Θ(n log n)

  8. Tree, Binary Tree, Binary Search Tree

1. Tree

• A tree is a hierarchical data structure consisting of nodes.
• The top node is called the root; nodes without children are called leaves.
• Each node (except the root) has exactly one parent.
• Trees are used in many applications like file systems, databases, and parsing expressions.
• Terminology:
  • Parent, Child, Sibling
  • Subtree: A tree formed by a node and its descendants.
  • Depth: Number of edges from root to node.
  • Height: Number of edges on the longest path from node to a leaf.

2. Binary Tree

• A binary tree is a tree where each node has at most two children: left and right.
• Types of binary trees:
  • Full Binary Tree: Every node has 0 or 2 children.
  • Complete Binary Tree: All levels are filled except possibly the last, which is filled from left to right.
  • Perfect Binary Tree: All internal nodes have two children and all leaves are at the same level.
• Common traversals:
  • Inorder (Left, Root, Right)
  • Preorder (Root, Left, Right)
  • Postorder (Left, Right, Root)
  • Level-order (Breadth-First Search)

3. Binary Search Tree (BST)

• A Binary Search Tree is a binary tree with the property:
  • For any node n:
    • All values in the left subtree < n.value
    • All values in the right subtree > n.value
• Operations:
  • Search: O(h) time complexity (h = tree height)
  • Insert: O(h)
  • Delete: O(h)
• Best case: Balanced BST → O(log n)
• Worst case: Unbalanced BST (like a linked list) → O(n)
• BSTs are used in:
  • Dynamic sets
  • Searching and sorting
  • Dictionary implementations