Data Structures And Algorithms

Data Structures And Algorithms Course Objectives:

• To equip students with the knowledge and skills to efficiently organize, manage, and process data.
• Teach students how to select and implement appropriate data structures, such as arrays, linked lists, trees, and graphs, to optimize data access and manipulation.
• It also focuses on developing algorithms for sorting, searching, and other computational tasks to enhance problem-solving capabilities.
• Participants can improve their programming efficiency, prepare for technical interviews, and build a strong foundation for advanced computer science studies.

The Course Highlights Why Data Structures And Algorithms Course In VyTCDC?

• The Data Structures and Algorithms course provides a comprehensive understanding of essential concepts, their importance, and classification. It delves into the implementation, operations, and use cases of various data structures, ensuring a solid foundation in their practical applications. The course covers the principles, applications, and implementation techniques of stacks and queues, crucial for efficient data management.
• Participants will explore binary trees, binary search trees, AVL trees, and graph algorithms, gaining insights into their structures and uses. The course also includes hashing techniques, collision resolution, and performance analysis, which are essential for optimizing data retrieval. Sorting and searching algorithms such as bubble sort, merge sort, quick sort, and binary search are thoroughly examined, highlighting their efficiency and application scenarios.
• A significant focus is placed on algorithm analysis, particularly time and space complexity, to understand and improve algorithm performance. The course introduces dynamic programming and greedy algorithms, emphasizing concepts and problem-solving strategies. Advanced data structures, including heaps, tries, and segment trees, are covered to equip students with knowledge of sophisticated data handling techniques.
• A significant focus is placed on algorithm analysis, particularly time and space complexity, to understand and improve algorithm performance. The course introduces dynamic programming and greedy algorithms, emphasizing concepts and problem-solving strategies. Advanced data structures, including heaps, tries, and segment trees, are covered to equip students with knowledge of sophisticated data handling techniques.

Curriculum:

• Introduction to Data Structures
• Basic Data Structures:
• Arrays, Strings, Stacks, Queues, List, Tree, Graph, Hash Tables
• Importance and applications of data structures in software development
• Basic math operations & Asymptotic analysis
• Math operations: addition, subtraction, multiplication, division, exponentiation
• Euclids GCD Algorithm
• Recursion
• Basic notations: Big-O, Big-theta, Big-Omega notations
• Algorithms Fundamentals
• Basics of algorithm analysis: Time complexity, space complexity
• Searching and sorting algorithms: Linear search, binary search, bubble sort, merge sort, quicksort, etc.
• Recursion and its applications in algorithms
• Greedy Algorithms
• Dynamic Programming
• Naive string searching
• Divide and Conquer
• Backtracking
• Data Structures
• Linear Data Structures
• Arrays and dynamic arrays
• Linked Lists: Singly linked lists, doubly linked lists, circular linked lists
• Stacks and Queues: Implementation, applications, and use cases
• Non-Linear Data Structures
• Trees: Binary trees, binary search trees (BSTs), AVL trees, B-trees
• Tree traversals, tree dynamic programming
• Graphs: Representation, traversal algorithms (BFS, DFS)
• Shortest path algorithms (Dijkstra's, Bellman-Ford, Floyd-Warshall)
• Minimum spanning tree (Prim and Kruskal algorithms)
• Network flow algorithms (Ford-Fulkerson, Edmonds-Karp)
• Biconnectivity in undirected graphs (bridges, articulation points)
• Strongly connected components in directed graphs
• Topological Sorting
• Advanced Data Structures
• Priority Queues and Heaps
• Hash Tables: Collision resolution techniques, hash functions
• Disjoint Sets and Union-Find
• Advanced Lists
• n-ary Tree
• Self-balancing Tree