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  • Data Structures and Algorithms in C++

Data Structures and Algorithms in C++

  • By Jeganathan
  • Data Structures & Algorithms
  • (0 Rating)
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    • Overview:
      This is an intermediate to expert level course intended for students and software professionals who already have a basic working knowledge of C++ programming.

      Who is it for?

      • Students pursuing Computer Science or related fields

      • Software professionals looking to strengthen their fundamentals

      • Anyone interested in writing efficient and optimized code in C++

      What you’ll learn?
      This course will help you understand and implement essential data structures (like arrays, linked lists, stacks, queues, trees, and graphs) and key algorithms (such as searching, sorting, recursion, and dynamic programming) using the C++ programming language. You’ll also learn how to analyze algorithm performance and write code that is both efficient and maintainable.

      Show More
      What Will You Learn?
      • By the end of this course, participants will be able to:
      • Core Programming Skills
      • Write efficient and optimized C code for solving complex computational problems.
      • Understand and implement recursion, iteration, and memory management in C.
      • Data Structures
      • Implement and use core data structures such as arrays, linked lists, stacks, queues, trees, heaps, hash tables, and graphs.
      • Choose the right data structure based on problem requirements and constraints.
      • Build custom data structures from scratch using C.
      • Algorithmic Thinking
      • Analyze time and space complexity using Big-O notation.
      • Solve problems using fundamental algorithms such as searching, sorting, and traversals.
      • Apply algorithm design paradigms like:
      • Greedy algorithms
      • Divide and conquer
      • Backtracking
      • Dynamic programming
      • Dynamic Programming Mastery
      • Identify problems that can be solved using dynamic programming.
      • Apply both top-down (memoization) and bottom-up (tabulation) approaches.
      • Solve classic DP problems like 0/1 Knapsack, LCS, and Matrix Chain Multiplication.
      • Problem-Solving & Interview Preparation
      • Build strong problem-solving skills through hands-on coding exercises.
      • Practice frequently asked technical interview questions.
      • Learn how to approach and solve competitive programming problems.
      • Project and Real-world Applications
      • Apply your skills to build a mini project involving multiple data structures and algorithms.
      • Understand the relevance of DS & Algos in real-world software development.

      Course Content

      Module 1: Data Structures and Algorithms

      • Overview of Data Structures and Algorithms
      • Time and Space Complexity
      • Recursion and Backtracking

      Module 2: Linear Data Structures

      • Arrays
      • Strings
      • Linked Lists
      • Singly Linked List
      • Doubly Linked List
      • Circular Linked List
      • Array based Stack Implementation
      • Linked list based Stack Implementation
      • Applications: Expression Evaluation, Syntax Parsing
      • Queues
      • Circular Queue
      • Priority Queue
      • Double-Ended Queue (Deque)

      Module 3: Non-Linear Data Structures

      • Trees
      • Binary Trees
      • Binary Search Trees
      • Tree Traversals
      • In order traversal
      • Pre-order Traversal
      • Post-order Traversal
      • Balanced Trees
      • AVL Trees
      • Red Black Trees
      • Heap Trees
      • Min Heap
      • Max Heap

      Module 4: Graphs

      • Representation: Adjacency List vs Matrix
      • Graph Traversals
      • Breath First Search Algorithm
      • Depth First Search Algorithm
      • Shortest Path
      • Cycle Detection

      Module 5: Dynamic Programming

      • Introduction to Dynamic Programming
      • Difference between Recursion, Memoization, and Tabulation
      • When to apply Dynamic Programming?
      • Top-down vs Bottom-up approaches
      • Space and Time Complexity Analysis in DP
      • Classic 1D DP Problems
      • Fibonacci Numbers (Recursion → Memoization → Tabulation)
      • Climbing Stairs (ways to reach the top)
      • Minimum Cost Climbing Stairs / Frog Jump Variants
      • House Robber Problem
      • Maximum Sum of Non-Adjacent Elements
      • 2D DP Problems
      • 0/1 Knapsack Problem
      • Subset Sum Problem
      • Equal Sum Partition
      • Count of Subsets with Given Sum
      • Target Sum Problem
      • String-based DP Problems
      • Longest Common Subsequence (LCS)
      • Longest Common Substring
      • Edit Distance / Levenshtein Distance
      • Longest Palindromic Subsequence
      • Minimum Insertions/Deletions to make Palindrome
      • Advanced DP Problems
      • Matrix Chain Multiplication
      • Palindrome Partitioning
      • Egg Dropping Problem
      • Boolean Parenthesization
      • Wildcard Matching / Regular Expression Matching
      • Optimal Binary Search Tree (OBST)
      • DP Optimization Techniques
      • State Compression (Reducing 2D → 1D)
      • Bitmask DP (for subset problems)
      • DP with Binary Search (e.g., LIS using patience sorting)
      • DP on Trees (Tree DP)
      • DP with Graphs (e.g., shortest path with constraints)

      Tags

      • Algorithms
      • C
      • Data structures
      • DSA
      • programming

      A course by

      J
      Jeganathan
      A
      admin

      Student Ratings & Reviews

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      Course Includes:

      • Price:
        ₹25,000.00 Original price was: ₹25,000.00.₹12,500.00Current price is: ₹12,500.00.
      • Instructor:Jeganathan
      • Duration:50 hours
      • Lessons:71
      • Students:0
      • Level:Intermediate
      ₹12,500.00 ₹25,000.00
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