Data Structure Syllabus |

  1. Introduction to Data Structures
  1. This topic explains what a data structure is and why it is important in programming.
  2. You learn how data is organized, stored, and accessed efficiently.
  3. Covers basic concepts like memory management, time complexity, and operations.
  4. Also introduces the difference between static and dynamic data structures.
  5. Helps you understand how choosing the right structure improves performance.
  6. Sets the foundation for all upcoming topics.
  1. Arrays
  1. Arrays store multiple elements of the same type in a continuous memory block.
  2. You learn indexing, inserting, updating, and deleting values.
  3. Covers one-dimensional, two-dimensional, and multi-dimensional arrays.
  4. Helps you understand how data is accessed quickly using index numbers.
  5. Learn common problems like searching and sorting on arrays.
  6. Widely used in competitive programming and system-level tasks.
  1. Strings
  1. Strings are sequences of characters, treated like character arrays.
  2. You learn string operations like concatenation, slicing, and comparison.
  3. Covers common algorithms like palindrome check, pattern matching, and frequency count.
  4. Helps in solving text-processing problems in coding interviews.
  5. You explore how strings are stored in memory and how immutable strings work.
  6. Very useful in building logic for real-world applications like search bars or validation.
  1. Linked Lists (Singly, Doubly, Circular)
  1. Linked lists store data in nodes, where each node points to the next.
  2. You learn insertion and deletion at beginning, end, and specific positions.
  3. Covers different types: singly, doubly, and circular linked lists.
  4. Useful when frequent addition or removal of elements is needed.
  5. Helps understand dynamic memory allocation and pointer manipulation.
  6. Common interview topic due to its logic-building nature.
  1. Stacks
  1. Stack follows the LIFO (Last In, First Out) method.
  2. Used in browser history, undo mechanisms, and expression evaluation.
  3. You learn push, pop, peek, and checking if the stack is empty/full.
  4. Covers implementation using arrays and linked lists.
  5. Helps you understand recursion as it is internally stack-based.
  6. Essential for solving problems involving nested structures.
  1. Queues
  1. Queue follows FIFO (First In, First Out) method.
  2. You learn enqueue, dequeue, front, rear, and queue status operations.
  3. Covers types: simple queue, circular queue, priority queue, and deque.
  4. Used in scheduling, call centers, ticket booking, etc.
  5. Helps in understanding real-life waiting line systems.
  6. Key concept in operating systems and networking.
  1. Trees (Binary Tree, BST, AVL Tree)
  1. Trees are hierarchical structures with parent-child relationships.
  2. You learn traversal methods: inorder, preorder, postorder, and level order.
  3. Covers Binary Trees, Binary Search Trees (BST), and AVL Trees.
  4. Helps in fast searching, insertion, and deletion operations.
  5. AVL Trees maintain balance to ensure high performance.
  6. Useful in implementing file systems, databases, and compilers.
  1. Graphs
  1. Graphs represent interconnected data using nodes (vertices) and edges.
  2. Covers types: directed, undirected, weighted, and unweighted graphs.
  3. Algorithms include BFS, DFS, Dijkstra, and Topological Sorting.
  4. Helps model real-world systems like Google Maps, networks, and social media.
  5. You learn adjacency matrix and adjacency list representations.
  6. One of the most important advanced topics in data structures.
  1. Searching Algorithms
  1. You learn how to find specific data in arrays or structures.
  2. Covers Linear Search and Binary Search in detail.
  3. Binary Search helps search in sorted lists with high efficiency.
  4. Explains time complexity and performance comparisons.
  5. Used widely in applications like search bars and finding IDs.
  6. Builds logical thinking for algorithm design.
  1. Sorting Algorithms
  1. Sorting helps arrange data in ascending or descending order.
  2. Covers Bubble, Selection, Insertion, Merge, Quick, and Heap Sort.
  3. You learn how each algorithm works with steps and examples.
  4. Understand best, worst, and average-case complexities.
  5. Sorting improves search performance and data organization.
  6. Very important for interviews and competitive programming.
  1. Hashing & Hash Tables
  1. Hashing helps in fast data retrieval using key-value pairs.
  2. You learn hash functions, collisions, and collision-resolving techniques.
  3. Used in dictionaries, databases, caches, and indexing.
  4. Helps achieve near-constant time access for search operations.
  5. Covers chaining and open addressing methods.
  6. Crucial for building high-performance applications.
  1. Heaps
  1. Heap is a special tree-based structure used for priority-based tasks.
  2. You learn min-heap and max-heap concepts.
  3. Helps in implementing priority queues.
  4. Used in job scheduling, ranking, and real-time systems.
  5. Heap Sort algorithm is based on this structure.
  6. Very efficient for selecting smallest/largest elements repeatedly.
  1. Tries (Prefix Trees)
  1. Trie is used to store dictionary-like datasets, especially strings.
  2. Helps in word search, auto-complete, and spell-check applications.
  3. Each character is stored as a node in a path.
  4. Provides fast retrieval of words based on prefixes.
  5. Efficient for handling large sets of similar words.
  6. Common in search engines and text editors.
  1. Advanced Data Structures
  1. Covers Red-Black Trees, B-Trees, Segment Trees, and Suffix Trees.
  2. Used mostly in large applications like databases and compilers.
  3. Helps manage and search complex data efficiently.
  4. You understand how advanced DS improve performance over simple DS.
  5. Mostly conceptual but important for higher-level learning.
  6. Increases your problem-solving strength for difficult coding tasks.

 

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