Data Structures & Algorithms Bootcamp

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Course Snapshot

Course Description

Complete C++ & DSA Curriculum — DSA Course – Foundation is a comprehensive 8–12 week program designed to transform beginners into interview-ready software engineers with a strong command over algorithms and data structures.

Program Structure

Duration: 12 weeks
Format: Live online classes + Classroom training + Hands-on coding assignments
Language: C++ (industry-standard for DSA & competitive programming)
Outcome: Build optimized solutions, crack technical interviews, and compete in coding challenges

DSA mastery is the #1 requirement for cracking technical interviews at top firms. Every coding round tests algorithms, data structures, optimization, and problem-solving under pressure. This course prepares you thoroughly with structured learning, live problem-solving, and interview-focused coding practice.

Who Should Enroll

Beginners starting their programming journey in the tech world
Students preparing for campus placements or technical interviews
Developers targeting roles at top tech giants like Google, Amazon, or Meta
Competitive programmers aiming for LeetCode, Codeforces, or CodeChef success
CS enthusiasts building strong foundations for AI, ML, cybersecurity, or game development

Course Outcomes & Skills You Master

Write optimized, efficient code with minimal time and space complexity
Solve complex algorithmic problems with confidence and precision
Ace technical interviews at Google, Amazon, Meta, Microsoft, and more
Compete in coding competitions and hackathons with advanced problem-solving
Understand memory management, recursion, and pointers at a deep level
Build scalable applications with optimized architecture and design
Strengthen logical and analytical thinking required for system design

This is your launchpad into elite software engineering and top-tier product roles.

Data Structures and Algorithm Course Curriculum

Master Tools, Techniques, and Real-World Applications

From C++ basics to advanced algorithms, this curriculum builds a structured progression. Start with language fundamentals, advance to data structures, then solve industry-level problems using competitive programming standards. Every module combines theory with hands-on coding and real-world applications. The result: interview-ready skills and the logical foundation required for system design and scalable architecture.

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This module lays the groundwork for understanding algorithms, problem-solving approaches, and programming basics.

Understanding how algorithms are structured and represented visually.
Importance of algorithm efficiency and design principles.

Developing problem-solving skills through pseudocode.
Creating flowcharts to represent algorithmic logic.

Overview of C++ syntax and structure.
Understanding variables, data types, and control flow (conditional statements and loops).

Mastering fundamental operations: arithmetic, logical, and relational.
Practice through hands-on coding challenges.

Introduction to functions: definition, creation, and function calls.
Understanding function parameters, return types, and scope.
Applying functions to modularize code and improve efficiency.

Declaring, initializing, and manipulating arrays.
Solving real-world problems using arrays (e.g., searching, sorting, and data storage).

This module focuses on essential programming techniques and core concepts of data structures and algorithms. You will enhance your coding efficiency using advanced C++ features, such as character arrays, pointers, recursion, and the Standard Template Library (STL).

Handling character arrays and string manipulation in C++.
Understanding pointers: memory allocation, management, and manipulation.
Dynamic memory allocation and deallocation (heap vs. stack memory).

Overview of fundamental data structures (lists, stacks, queues, etc.).
Introduction to basic algorithms: sorting, searching, and traversal techniques.
Analyzing time and space complexity for algorithm optimization.

Utilizing STL components such as vectors, sets, maps, and priority queues.
Implementing common algorithms (sorting, searching) using STL for faster and more efficient coding.

Understanding bit masking and its applications in algorithm design.
Advanced string manipulation techniques: pattern matching, substring finding, and encoding.
Optimizing string algorithms using bitwise operations and efficient data handling.

Introduction to recursion and its role in problem-solving.
Understanding recursive function calls, base cases, and stack behavior.
Analyzing recursive algorithms (e.g., factorial, Fibonacci series, backtracking problems).

This module delves deeper into advanced recursion concepts, dynamic programming, and backtracking techniques. You will learn to optimize algorithms for complex problems while analyzing their efficiency using Big O notation.

Advanced recursion concepts and techniques.
Understanding recursive optimization strategies for solving complex problems.
Applications of recursion in various algorithmic scenarios.

Introduction to dynamic programming as an extension of recursion.
Understanding memoization and tabulation techniques for optimization.
Applying dynamic programming to real-world problems (e.g., knapsack, longest common subsequence).

Solving subset-related problems using recursive approaches.
Implementing dynamic programming on subsets to optimize recursive solutions.
Problems involving subsets: subset sum, partitioning, and combinations.

Introduction to backtracking and its problem-solving approach.
Implementing recursive backtracking for problems like N-Queens, Sudoku, and permutations.
Optimizing recursive calls by pruning unnecessary paths.

Understanding and analyzing algorithm efficiency using Big O notation.
Practical applications of time and space complexity analysis in algorithm design.
Comparing different algorithmic approaches to select the most efficient solution.

This module focuses on the core concepts of searching and sorting, essential for efficient data manipulation and optimization. You will learn and practice both fundamental and advanced algorithms to build a solid foundation in problem-solving.

Understanding the need for searching algorithms in data structures.
Overview of linear and binary search algorithms.
Linear Search: Simple search through an entire dataset.
Binary Search: Optimized search for sorted datasets using divide-and-conquer.
Practice Problems: Implementing and optimizing search algorithms on various data structures.

Importance of sorting algorithms in improving data accessibility and efficiency.
Overview of sorting approaches and their applications.

Bubble Sort: Repeatedly swapping adjacent elements to sort the list.
Selection Sort: Selecting the smallest element and placing it at the beginning of the list.
Insertion Sort: Inserting elements into their correct position in a sorted portion of the list.
Merge Sort: Using the divide-and-conquer approach to recursively sort and merge subarrays.
Practice Problems: Sorting arrays and custom data structures using these algorithms.

Quick Sort: Efficient, recursive partition-based sorting algorithm.
Heap Sort: Using the heap data structure to build a sorted list.
Bucket Sort: Dividing elements into buckets and sorting each bucket individually.
Cycle Sort: Minimizing the number of writes to achieve in-place sorting.
Radix Sort: Non-comparative sorting based on digit-by-digit processing.
Practice Problems: Implementing advanced algorithms for large data sets.

This module introduces matrices, their operations, and various problem-solving techniques. You will learn to implement and manipulate matrices in C++, perform key operations, and solve complex problems using efficient algorithms.

Understanding the concept of matrices and their representation in C++.
Overview of matrix operations and their significance in programming.
Implementing matrices using multidimensional arrays.

Declaring and initializing multidimensional matrices in C++.
Passing matrices as function arguments and handling them efficiently.

Transpose of a Matrix: Interchanging rows and columns.
Matrix Rotation: Rotating matrices by 90 degrees clockwise or counterclockwise.
Matrix Multiplication: Understanding and implementing matrix product algorithms.

Spiral Traversal: Traversing a matrix in a spiral order.
Snake Pattern Printing: Printing matrix elements in a snake-like pattern.
Practice Problems: Solving real-world problems that require complex matrix manipulation and traversal.

This module focuses on the principles of Object-Oriented Programming (OOP) and advanced data structures such as linked lists, stacks, and queues. You will learn how to apply OOP concepts in C++ to design efficient, scalable, and maintainable code while gaining expertise in key data structures and their applications.

Understanding the principles of OOP: encapsulation, inheritance, polymorphism, and abstraction.
Benefits of OOP in software design and development.

Creating and using classes and objects.
Understanding constructors, destructors, and access specifiers.
Implementing inheritance, polymorphism, and method overriding.
Practical use cases of OOP in solving real-world problems.

Introduction to Linked Lists: Understanding nodes and pointers.
Types of linked lists: singly, doubly, and circular linked lists.
Implementation of operations: insertion, deletion, traversal, and searching.
Applications of linked lists in dynamic data management.

Understanding stack data structure and LIFO (Last In, First Out) principle.
Implementing stack operations: push, pop, peek, and isEmpty.
Applications of stacks: expression evaluation, recursion handling, and function calls.

Understanding queue data structure and FIFO (First In, First Out) principle.
Implementing standard and circular queues.
Operations on queues: enqueue, dequeue, front, and isEmpty.
Applications of queues: task scheduling, buffer management, and breadth-first search (BFS).

In this module, you will learn about the powerful C++ Standard Template Library (STL), which provides built-in data structures and algorithms to simplify coding and enhance efficiency. You will understand how to leverage STL for rapid problem-solving and algorithm implementation in competitive programming and real-world applications.

Overview of the C++ Standard Template Library.
Understanding key components of STL: containers, iterators, and algorithms.

Vectors: Dynamic arrays with efficient insertion and access.
Lists: Implementation of doubly linked lists.
Stacks and Queues: Pre-built data structures following LIFO and FIFO principles.
Sets and Maps: Efficient storage and retrieval of unique elements and key-value pairs.
Applications of STL in various problem-solving scenarios.

Hands-on practice in using STL containers and iterators.
Implementing algorithms like sorting, searching, and traversal using STL.
Enhancing code readability and reducing development time with STL functions.

Solve real-world problems and competitive programming challenges using STL.
Focus on optimizing solutions with STL features such as built-in sorting, priority queues, and hash maps.

This module focuses on tree data structures, a fundamental concept in computer science. You will learn about various types of trees, their applications, and how to implement and manipulate them effectively. Special emphasis is placed on Binary Search Trees (BST), traversal techniques, and solving real-world problems.

Understanding the concept of trees and their hierarchical structure.
Overview of tree-based data organization and applications in areas like databases, file systems, and search engines.

Introduction to binary trees and their properties.
Understanding nodes, parent-child relationships, and tree height.

Inorder Traversal: Left subtree → Root → Right subtree.
Preorder Traversal: Root → Left subtree → Right subtree.
Postorder Traversal: Left subtree → Right subtree → Root.
Level Order Traversal: Traverse nodes level by level.
Spiral (Zigzag) Traversal: Alternating direction traversal at each level.
Applications of different traversal methods in problem-solving.

Introduction to BST and its properties.
Implementing search operations in BST.
Understanding and implementing insertion, deletion, and searching operations in BST.

Solve problems involving tree traversal, height calculation, pathfinding, and balanced trees.
Apply concepts to scenarios such as hierarchical data representation, expression trees, and interval trees.

This module introduces advanced data structures and algorithmic techniques, including heaps, hashmaps, graphs, and tries. You will also deepen your understanding of dynamic programming and its applications in solving complex problems efficiently.

Revisiting matrix operations, including traversal and manipulation.
Solving advanced matrix-based problems such as pathfinding and dynamic data storage.

Understanding the concept of heaps (min-heap and max-heap).
Applications of heaps in scenarios like priority queues and heap sort.
Implementing heap operations: insertion, deletion, and heapify.

Introduction to hashmaps (unordered maps in C++).
Implementing hashmaps for efficient key-value storage and retrieval.
Applications of hashmaps in tasks such as frequency counting, caching, and lookups.

Understanding the fundamentals of graphs: vertices, edges, adjacency list, and adjacency matrix.
Implementing graph algorithms:
- Depth First Search (DFS)
- Breadth First Search (BFS)
- Shortest path algorithms (e.g., Dijkstra’s, Bellman-Ford).
Applications of graphs in areas such as networking, route optimization, and social networks.

Exploring the trie (prefix tree) data structure.
Implementing operations like insert, search, and delete for string management.
Applications of tries in problems such as autocomplete, dictionary storage, and pattern matching.

Revisiting dynamic programming (DP) principles.
Identifying overlapping subproblems and optimal substructure.
Solving complex problems using DP techniques (e.g., longest common subsequence, knapsack problem, matrix chain multiplication).

Solve problems using heaps, hashmaps, graphs, tries, and dynamic programming.
Apply concepts to real-world scenarios like scheduling, data indexing, and graph traversal.

This module focuses on greedy algorithms, an important problem-solving strategy, and revisits the trie data structure and dynamic programming techniques to tackle complex optimization problems efficiently.

Principles of Greedy Approach:
- Selecting the locally optimal solution at each step with the aim of finding a globally optimal solution.
Applications of Greedy Algorithms:
- Problems such as activity selection, coin change, job scheduling, and Huffman coding.
Greedy vs. Dynamic Programming:
- Understanding when greedy algorithms provide optimal results compared to dynamic programming approaches.
Problem-Solving:
- Implementing and solving problems that can be optimized using the greedy approach.

Introduction to Tries:
- Efficiently storing and retrieving strings, especially for dictionary or prefix-based searches.
Operations on Tries:
- Insert, search, and delete operations in a trie.
Applications:
- Autocomplete systems, string matching, and data indexing.

Advanced DP Concepts:
- Reinforcing principles of dynamic programming with complex scenarios.
Problem-Solving with Dynamic Programming:
- Tackling real-world optimization problems like maximum profit, pathfinding, and scheduling tasks.
Comparative Analysis:
- Identifying problems where DP is more suitable than greedy algorithms.

Solve a variety of problems using greedy algorithms, tries, and dynamic programming.
Apply these concepts to optimize solutions in competitive programming and system design scenarios.

This module is designed to help you prepare for technical interviews by covering commonly asked questions in data structures and algorithms (DSA). You will practice solving real-world problems, participate in mock interviews, and gain valuable insights to increase your chances of success in coding interviews.

Explore frequently asked DSA questions from top tech companies such as Google, Amazon, Microsoft, and others.
Topics include:
- Array and string manipulation.
- Searching and sorting algorithms.
- Recursion, dynamic programming, and backtracking.
- Tree and graph traversal algorithms.
- Optimized solutions using heaps, hashmaps, and other advanced data structures.

Practice solving real-world problems that require optimized solutions.
Focus on understanding problem requirements, constraints, and performance optimization.

Participate in mock interviews conducted by industry experts.
Experience the pressure of time-bound problem-solving, improving your ability to think on your feet.
Receive detailed feedback on your approach, coding style, and problem-solving techniques.

Learn strategies to effectively approach coding interviews, including time management and algorithm selection.
Understand how to explain your thought process, optimize your code, and handle follow-up questions.