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More Academy


Certification Program
Data Structure

Data structures are organized ways to store, manage, and access data efficiently. They include types like arrays, linked lists, stacks, queues, and trees. Understanding data structures is essential for writing optimized code and solving complex programming problems.


Offline sessions | Mentor Support | Placement Assistance


Data Structure

Who Should Join This Course?

This course is perfect for students, beginners, and career switchers who want to strengthen their programming logic and master data structures and algorithms (DSA) from scratch. If you’re preparing for technical interviews, competitive coding rounds, or aiming to get into top product-based companies, this course will be a valuable asset. It is also ideal for engineering and computer science students looking to boost their academic performance and practical understanding. Even working professionals who wish to revisit core CS fundamentals or prepare for advanced roles in software development will benefit from the step-by-step approach of this course.




Learning Modules

Dive into fundamental linear data structures—Stacks (LIFO) and Queues (FIFO). Learn how to implement them using arrays and linked lists. Understand practical applications like expression evaluation (infix to postfix), undo operations, and scheduling systems. Solve real-life logical problems with step-by-step coding walkthroughs and time-space analysis. You’ll also encounter variations like circular queues and multiple stack implementations. Strengthen your algorithmic thinking with interview-style challenges and develop a strong foundation for recursion and backtracking in future modules.

Discover double-ended queues (Deques) and how they can optimize both ends of linear access. Master the fundamentals of recursion, including base and recursive cases, stack memory, and call tracing. Learn to solve classic problems such as factorial, Fibonacci series, and palindrome checks using recursion. This module emphasizes logical structuring and problem-solving approaches, using recursion in puzzles, games, and algorithm design. Visualizations and code demos will enhance your understanding, helping you confidently transition to backtracking in the next phase.

Explore the power of backtracking—a problem-solving technique based on recursion and constraint satisfaction. Learn how to build generalized backtracking templates to solve puzzles and optimization problems like the N-Queens, Maze solving, Sudoku, and permutation generation. Understand decision trees, pruning, and bounding to avoid unnecessary computations. Analyze complexity and scalability. Through visual traces and guided code labs, you'll build strong logic and adaptability, empowering you to confidently handle questions in competitive programming and technical interviews.

Master different searching techniques starting from simple linear search to efficient binary, ternary, and exponential searches. Understand the logic, implementation, and use cases for each approach. Learn how to build and traverse decision trees using searching logic. Explore real-world applications such as autocomplete systems, number games, and database queries. Gain clarity on best-case, worst-case, and average-case complexity analysis. Apply these techniques in practice through competitive coding challenges and interview scenarios.

Get hands-on with a wide range of sorting techniques—bubble, selection, insertion, merge, quick, and heap sort. Understand how each algorithm works through visual animations, comparisons, and code tracing. Evaluate performance using time complexity and stability metrics. Explore where and when to use each algorithm in real-world scenarios like ranking systems, inventory management, and data pipelines. Learn about in-place and non-in-place sorting, recursive vs. iterative techniques, and sort optimizations. Practice through real interview patterns and challenge problems.

Uncover hierarchical data structures starting with general trees, then delve into binary trees and binary search trees (BST). Learn tree creation, traversal techniques (inorder, preorder, postorder), and recursive strategies. Implement insertion, deletion, and search operations in BSTs. Understand applications in databases, file systems, and compilers. Solve problems like height, diameter, balanced trees, and lowest common ancestor (LCA). This module bridges recursion with structure, helping you visualize and build efficient, hierarchical data models with confidence.

Master min-heaps, max-heaps, and priority queues—specialized tree-based structures used in scheduling, pathfinding, and data compression algorithms. Learn heapify, insert, delete, and peek operations. Solve problems like finding the Kth smallest/largest element, merging K sorted arrays, and heapsort. Understand how heaps differ from BSTs and where they shine in real-world tasks. Build intuition with memory diagrams, dry runs, and application-based coding labs. Get a step closer to mastering DSA with strong optimization tools.

Conquer one of the most important topics in DSA—graphs. Learn how to represent and traverse graphs using BFS and DFS. Detect cycles, find shortest paths (Dijkstra's, Bellman-Ford), and apply graph theory in networking, games, and maps. Understand hashing, collision handling, open addressing, and hash functions for fast lookups and data mapping. Solve problems involving maps, sets, anagrams, and frequency counters. This module equips you to work with complex, interconnected data using optimal space and time techniques.

Get certified and improve your career opportunities

More Academy certificates provide a strong foundation for entering the software development industry, giving you an edge in landing top jobs worldwide. With a **Certification in Data Structures**, you can build a promising career in software engineering, qualifying for roles like **Software Developer, Data Engineer, Algorithm Engineer**, or **Systems Developer**. This certification opens doors to exciting opportunities in tech companies and startups looking for skilled problem-solvers and efficient data handlers.

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