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Explore Data Structure and Algorithm with C++

The Data Structures & Algorithms with C++ course is designed to provide students with a deep understanding of the core concepts of data structures and algorithms, and their implementation using C++. This course covers key data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Additionally, it explores fundamental algorithm design techniques, including sorting, searching, dynamic programming, and graph algorithms.

Students will also learn how to analyze the time and space complexity of algorithms using Big O notation and how to optimize algorithms for better performance. By the end of the course, students will be able to write efficient C++ programs, implement complex data structures, and solve real-world problems using various algorithms.

This course is ideal for students pursuing computer science, software engineering, or any related field, and for anyone who wants to strengthen their problem-solving skills using C++.

Key Learning Outcomes

By the end of the course, students will:

  1. Understand the core concepts of data structures and algorithms.
  2. Be able to implement common data structures such as arrays, linked lists, stacks, queues, trees, graphs, and hash tables using C++.
  3. Master popular algorithms like sorting, searching, dynamic programming, and graph traversal.
  4. Be proficient in analyzing and optimizing algorithms using Big O notation.
  5. Gain practical experience by solving real-world problems and working on a capstone project.

Career Opportunities

Graduates of this course can pursue careers such as:

  • Software Engineer
  • Data Structures Engineer
  • Algorithm Developer
  • Competitive Programmer
  • Technical Consultant
  • System Architect
  • C++ Fundamentals
    • Introduction to C++ and setting up the development environment
    • Basic syntax, variables, data types, operators, and control flow
    • Functions, arrays, pointers, and references
  • Object-Oriented Programming in C++
    • Classes and objects
    • Constructors, destructors, and member functions
    • Inheritance, polymorphism, encapsulation, and abstraction
  • What are Data Structures?
    • Types of data structures: Linear vs. non-linear
    • Importance of data structures in algorithm design
    • Time and space complexity analysis (Big O notation)
  • Arrays
    • Defining and initializing arrays
    • Multi-dimensional arrays
    • Operations on arrays: Insertion, deletion, searching, and sorting
  • Linked Lists
    • Singly linked list: Creation, insertion, deletion, and traversal
    • Doubly linked list and circular linked list
    • Applications of linked lists
  • Stacks
    • Stack operations: Push, pop, peek
    • Applications of stacks (e.g., expression evaluation, recursion)
    • Implementing stacks using arrays and linked lists
  • Queues
    • Queue operations: Enqueue, dequeue, front
    • Types of queues: Linear queues, circular queues, and double-ended queues (Deque)
    • Applications of queues (e.g., scheduling, breadth-first search)
  • Introduction to Trees
    • Binary trees: Definition and properties
    • Tree traversal algorithms: Preorder, inorder, postorder
    • Binary search trees (BST): Insertion, deletion, search operations
  • Balanced Trees
    • AVL Trees and Red-Black Trees
    • Self-balancing property of AVL trees
    • Insertion and deletion operations in balanced trees
  • Heaps
    • Introduction to heaps: Max heap and min heap
    • Heap operations: Insertion, deletion, heapify
    • Applications of heaps (e.g., priority queues, heap sort)
  • Introduction to Graphs
    • Types of graphs: Directed, undirected, weighted, unweighted
    • Graph representations: Adjacency matrix, adjacency list
    • Graph traversal algorithms: Depth-first search (DFS), breadth-first search (BFS)
  • Shortest Path Algorithms
    • Dijkstra’s algorithm
    • Bellman-Ford algorithm
    • Floyd-Warshall algorithm
  • Minimum Spanning Tree (MST)
    • Kruskal’s algorithm
    • Prim’s algorithm
  • Introduction to Hashing
    • What is hashing?
    • Hash function and hash table
    • Collisions and collision resolution methods (open addressing, chaining)
  • Applications of Hashing
    • Implementing efficient search operations
    • Hash maps and hash sets in C++
    • Solving problems using hashing
  • Sorting Algorithms
    • Bubble Sort, Selection Sort, Insertion Sort
    • Merge Sort, Quick Sort, Heap Sort
    • Radix Sort and Counting Sort
    • Time and space complexity of sorting algorithms
  • Searching Algorithms
    • Linear Search and Binary Search
    • Searching in sorted arrays and linked lists
    • Time complexity analysis of searching algorithms
  • Greedy Algorithms
    • Greedy method overview
    • Applications of greedy algorithms: Activity selection, Huffman coding
  • Divide and Conquer
    • Principles of divide and conquer
    • Merge Sort, Quick Sort, Binary Search as divide and conquer algorithms
  • Dynamic Programming
    • Introduction to dynamic programming
    • Solving problems using dynamic programming: Fibonacci sequence, knapsack problem, longest common subsequence
  • Solving Complex Problems
    • Problem-solving strategies for coding interviews
    • Practice with data structure and algorithm problems
    • Working on platforms like LeetCode, Codeforces, HackerRank, and others
  • Algorithm Optimization
    • Optimizing algorithms for time and space efficiency
    • Advanced data structures for efficient algorithms (Trie, Segment Tree, Fenwick Tree)
  • Capstone Project
    • Apply the learned data structures and algorithms to solve a real-world problem
    • Work on a comprehensive project that requires the integration of various data structures and algorithms
  • Final Exam and Certification
    • Final exam to assess knowledge and application of data structures and algorithms
    • Certificate of completion upon successful project submission and exam completion

Short-term Course: 3 to 4 months

Comprehensive Course: 5 to 6 months

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