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Hands-on Mastery in Data Structures and Algorithms

Don't just memorize algorithms. Master data structures, Big-O asymptotic analysis, pointers, recursion, trees, graphs, dynamic programming, and production algorithmic problem-solving with your dedicated in-course DSA AI Agent coach.

12 Modules ā€¢ 5 Production Projects
12 Modules
AI FearFilter Faculty

What You Will Learn

Analyze algorithmic efficiency with precision using Big-O, Big-Omega, and Big-Theta asymptotic bounds across time and space.
Build and manipulate sequential memory layouts, dynamic arrays, sliding windows, and two-pointer traversal algorithms.
Implement and optimize core searching and sorting algorithms including Binary Search, MergeSort, QuickSort, and QuickSelect.
Design singly and doubly linked list architectures, detecting cycles and reversing structures in O(1) auxiliary space.
Leverage LIFO Stacks and FIFO Queues to build expression evaluators, monotonic sequences, and breadcrumbs navigation.
Engineer high-throughput Hash Tables with open addressing and separate chaining collision resolution achieving O(1) average lookup.
Formulate recursive divide-and-conquer solutions and combinatorial search algorithms with backtracking and pruning.
Traverse, rebalance, and query hierarchical tree structures including Binary Search Trees, AVL Trees, and Prefix Tries.
Model complex real-world relationships with directed, undirected, and weighted graphs using adjacency matrices and lists.
Solve shortest path network problems with Dijkstra and Bellman-Ford algorithms and compute minimum spanning trees with Kruskal.
Decompose overlapping subproblems and optimal substructures using memoization and dynamic programming table tabulation.
Complete 5 production portfolio projects and pass the Module 12 Capstone Challenge evaluated by the DSA AI Agent.

Curriculum & Weekly Roadmap

12 Structured Modules

Module 1 ā€” Foundations of Algorithmic Thinking & Asymptotic Complexity (Big-O)

  • What is an Algorithm? Inputs, Outputs, Correctness & Determinism
  • Time Complexity vs Space Complexity: Why Raw Clock Speed Lies
  • Asymptotic Notations: Big-O (O), Big-Omega (Ī©), and Big-Theta (Ī˜)
  • Dominant Terms, Drop Constants, and Common Complexity Classes
  • Space Complexity: Auxiliary Space, Call Stack Frames & In-Place Algorithms

Module 2 ā€” Sequential Data Structures: Static Arrays, Dynamic Arrays & Strings

  • Contiguous Memory Allocation, Cache Locality & O(1) Random Access Indexing
  • Dynamic Arrays: Capacity, Growth Factor, and Amortized O(1) Insertion Analysis
  • Strings as Byte Arrays: Immutability, String Builders, and ASCII/Unicode Layout
  • Two-Pointer Technique: Oppositely Directed Pointers & Equi-Directed Pointers
  • Sliding Window Technique: Fixed-Length Windows & Dynamically Resized Windows

Module 3 ā€” Searching & Sorting Algorithms: Comparative & Non-Comparative Sorting

  • Linear Search vs Binary Search: Halving Search Spaces & Preconditions
  • Binary Search on Answer Spaces: Monotonic Predicates & Discrete Optimization
  • Quadratic Sorting: Bubble Sort, Selection Sort, and Insertion Sort Mechanics
  • Divide-and-Conquer Sorting: Merge Sort (Stable, O(N log N)) & Recursion Trees
  • QuickSort: Partitioning Schemes (Lomuto vs Hoare), Pivot Selection & Worst-Case O(NĀ²)

Module 4 ā€” Linked Data Structures: Singly, Doubly & Circular Linked Lists

  • Pointers & References: Nodes, Next Links & Non-Contiguous Heap Allocation
  • Singly Linked Lists: Node Definition, Head Pointers & Traversal Mechanics
  • Singly Linked List Mutations: O(1) Head Insertion vs O(N) Tail Insertion
  • Doubly Linked Lists: Bidirectional Traversal, Prev Pointers & O(1) Node Deletion
  • Fast and Slow Pointer Technique (Floyd Cycle Detection): Detecting Loops & Finding Midpoints

Module 5 ā€” Stacks & Queues: LIFO, FIFO, Deques & Monotonic Structures

  • The Stack Abstract Data Type: LIFO Principle, Push, Pop, Peek & Array/Node Backing
  • Stack Applications: Balanced Parentheses Validation & Call Stack Simulation
  • The Queue Abstract Data Type: FIFO Principle, Enqueue, Dequeue & Circular Buffer Backing
  • Double-Ended Queue (Deque): Double-Ended Insertion/Deletion & Sliding Window Maxima
  • Monotonic Stacks & Monotonic Queues: Next Greater Element & Histogram Area Optimization

Module 6 ā€” Hash-Based Data Structures: Hash Tables, Hash Sets & Hash Maps

  • Hash Functions: Determinism, Uniform Distribution & Bitwise Operations
  • Collision Resolution: Separate Chaining (Linked Lists & Red-Black Trees)
  • Collision Resolution: Open Addressing (Linear Probing, Quadratic Probing & Double Hashing)
  • Load Factor & Dynamic Rehashing: Thresholds, Table Doubling & Amortized O(1) Lookups
  • Hash Maps vs Hash Sets: Key-Value Associations, Uniqueness Guarantees & Real-World Caching

Module 7 ā€” Recursion & Algorithmic Paradigms: Divide-and-Conquer & Backtracking

  • Mathematical Induction & The Anatomy of a Recursive Function: Base Cases vs Recursive Steps
  • Call Stack Mechanics: Activation Records, Stack Overflow Risks & Tail Call Optimization
  • Divide-and-Conquer Paradigm: Subproblem Partitioning, Conquering & Master Theorem Intuition
  • Backtracking Paradigm: State Space Trees, Systematic Exploration & Backtracking Decisions
  • Branch Pruning: Constraint Satisfaction, Early Termination & The N-Queens Problem

Module 8 ā€” Hierarchical Data Structures: Trees, Binary Trees & Binary Search Trees

  • Tree Terminology: Roots, Nodes, Edges, Leaves, Subtrees, Depth, Height & Degrees
  • Binary Tree Fundamentals: Full, Complete, Perfect & Degenerate Binary Trees
  • Tree Traversals: Depth-First Search (Pre-Order, In-Order, Post-Order) & Level-Order BFS
  • Binary Search Trees (BST): BST Invariant Property, O(log N) Search, Insert & In-Order Sorting
  • BST Deletion Mechanics: Deleting Nodes with Zero, One, or Two Children (In-Order Successor)

Module 9 ā€” Advanced Trees: Self-Balancing Trees, Heaps, Priority Queues & Tries

  • Tree Imbalance & Worst-Case Degeneracy: The Need for Self-Balancing BSTs (AVL & Red-Black Trees)
  • AVL Tree Rotations: Balance Factors, Left Rotations, Right Rotations & Double Rotations
  • Binary Heaps: Max-Heaps, Min-Heaps & Array Representation of Complete Binary Trees
  • Heap Operations: Heapify, Sift-Up, Sift-Down, O(log N) Push/Pop & O(N) Heap Construction
  • Prefix Tries: Node Structure, Character Edges, Prefix Matching & Autocomplete Engineering

Module 10 ā€” Disjoint Set Systems: Union-Find (DSU) & Connected Components

  • Disjoint Set Abstract Data Type: Partitioning Elements into Mutually Exclusive Sets
  • Naive Union-Find: Parent Array Representation, Find Operation & Tree Height Degeneration
  • Union by Rank and Union by Size: Preventing Tall Trees & Maintaining Shallow Topologies
  • Path Compression: Flattening Trees on Find Queries for Near-Constant Amortized Cost
  • Inverse Ackermann Complexity: Ī±(N) Bounds, Cycle Detection in Undirected Graphs & Connectivity Queries

Module 11 ā€” Graph Algorithms: Representations, Traversals & Shortest Paths

  • Graph Theory Fundamentals: Vertices, Edges, Directed, Undirected, Weighted & Cyclic Graphs
  • Graph Representations: Adjacency Matrix (O(VĀ²) space) vs Adjacency List (O(V + E) space)
  • Graph Traversals: Breadth-First Search (BFS for Shortest Paths in Unweighted Graphs)
  • Graph Traversals: Depth-First Search (DFS for Connected Components & Topological Sorting)
  • Single-Source Shortest Paths: Dijkstra Algorithm with Min-Heaps & Bellman-Ford Negative Weight Edge Detection

Module 12 ā€” Dynamic Programming (DP) & Production Algorithmic Capstone Challenge

  • The Core of Dynamic Programming: Overlapping Subproblems & Optimal Substructure Properties
  • Top-Down Memoization vs Bottom-Up Tabulation: Recursion Cache vs Iterative State Table
  • 1D Dynamic Programming: Fibonacci, Climbing Stairs & Maximum Subarray (Kadane Algorithm)
  • 2D Dynamic Programming: 0/1 Knapsack Problem, Longest Common Subsequence & Grid Paths
  • Module 12 Production Capstone: Autonomous Task Scheduler & Real-Time Priority Dispatcher

Who This Course Is For

Aspiring Software Engineers, Backend Developers, Systems Architects, Computer Science Students, and Engineering Candidates aiming to master data structures and algorithms from scratch with interactive visualizations and Socratic AI coaching.

Key Skills Developed:

Time & Space Complexity (Big-O, Big-Omega, Big-Theta)Static & Dynamic Arrays, Amortized Scaling & StringsSearching & Sorting (Binary Search, MergeSort, QuickSort)Linked Lists (Singly, Doubly, Fast/Slow Pointer)Stacks & Queues (Monotonic, BFS/DFS Fundamentals)Hash Tables, Collision Resolution & Hash MapsRecursion, Backtracking & Branch PruningBinary Trees, BSTs & Balanced AVL/Red-Black TreesPriority Queues, Binary Heaps & Prefix TriesDisjoint Set Union (Union-Find) with Path CompressionGraph Algorithms (BFS/DFS, Dijkstra, Bellman-Ford, A*)Dynamic Programming, Memoization & Autonomous Capstone

Course Faculty & Development

AI FearFilter Faculty

Algorithms & Systems Architecture Team

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