Data Structure

Data Structure

Data Structure is very important subject in the computer science branch,
Product based company demand a strong understanding of Data structure and algorithm
Linear Data Structure Introduction (2L):
Why we need data structure?
10
Concepts of data structures: a) Data and data structure b) Abstract Data Type and Data Type. Algorithms and programs, basic idea of pseudo-code.
Algorithm efficiency and analysis, time and space analysis of algorithms – order notations.
Array (2L):
Different representations – row major, column major .
Sparse matrix – its implementation and usage. Array representation of polynomials.
Linked List (4L):
Singly linked list, circular linked list, doubly linked list, linked list representation of polynomial and applications.
[7L] Linear Data Structure [Stack and Queue (5L):
Stack and its implementations (using array, using linked list), applications.
Queue, circular queue, dequeue. Implementation of queue- both linear and circular (using array, using linked list), applications.
Recursion (2L):
Principles of recursion – use of stack, differences between recursion and iteration, tail recursion. Applications – The Tower of Hanoi, Eight Queens Puzzle.
[15L] Nonlinear Data structures Trees (9L):
Basic terminologies, forest, tree representation (using array, using linked list).
Binary trees – binary tree traversal (pre-, in-, post- order), threaded binary tree (left, right, full) – non-recursive traversal algorithms using threaded binary tree, expression tree.
Binary search tree- operations (creation, insertion, deletion, searching). Height balanced binary tree – AVL tree (insertion, deletion with examples only). B- Trees – operations (insertion, deletion with exa mples only).
Graphs (6L):
Graph definitions and concepts (directed/undirected graph, weighted/un-weighted edges, sub-graph, degree, cut-vertex/articulation point, pendant node, clique, complete graph, connected components – strongly conne cted component, weakly connected component, path, shortest path, isomorphism).
Graph representations/storage implementations – adj acency matrix, adjacency list, adjacency multi-list.
Graph traversal and connectivity – Depth-first sear ch (DFS), Breadth-first search (BFS) – concepts of edges used in DFS and BFS (tree-edge, back-edge, cross-edge, forward-edge), applications.
Minimal spanning tree – Prim’s algorithm (basic ide a of greedy methods).
Searching, Sorting (10L):
Sorting Algorithms (5L): Bubble sort and its optimizations, insertion sort, shell sort, selection sort, merge sort, quick sort, heap sort (concept of max heap, application – priority queue), radix sort.
Searching (2L): Sequential search, binary search, interpolation search.
Hashing (3L): Hashing functions, collision resolution techniques.

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Recommended books:
1. “Data Structures And Program Design In C”, 2/E by Robert L. Kruse, Bruce P. Leung.
2. “Fundamentals of Data Structures of C” by Ellis Hor owitz, Sartaj Sahni, Susan Anderson-freed.
3. “Data Structures in C” by Aaron M. Tenenbaum.
4. “ Data Structures” by S. Lipschutz.
5. “Data Structures Using C” by Reema Thareja.
6. “Data Structure Using C”, 2/e by A.K. Rath, A. K. J agadev.
7. “ Introduction to Algorithms” by Thomas H. Cormen, Ch arles E. Leiserson, Ronald L. Rivest, Clifford Stein.
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