CS205 Data Structures

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Course code Course Name L-T-P-Credits Year of

Introduction
CS205 Data Structures 3-1-0-4 2016
Pre-requisite: B101-05 Introduction to Computing and Problem Solving
Course Objectives
1. To impart a thorough understanding of linear data structures such as stacks, queues and their
applications.
2. To impart a thorough understanding of non-linear data structures such as trees, graphs and their
applications.
3. To impart familiarity with various sorting, searching and hashing techniques and their
performance comparison.
4. To impart a basic understanding of memory management.
Syllabus
Introduction to various programming methodologies, terminologies and basics of algorithms
analysis, Basic Abstract and Concrete Linear Data Structures, Non-linear Data Structures, Memory
Management, Sorting Algorithms, Searching Algorithms, Hashing.
Expected Outcome:
Students will be able to
1. compare different programming methodologies and define asymptotic notations to analyze
performance of algorithms.
2. use appropriate data structures like arrays, linked list, stacks and queues to solve real world
problems efficiently.
3. represent and manipulate data using nonlinear data structures like trees and graphs to design
algorithms for various applications.
4. illustrate and compare various techniques for searching and sorting.
5. appreciate different memory management techniques and their significance.
6. illustrate various hashing techniques.
Text Books:
1. Samanta D., Classic Data Structures, Prentice Hall India, 2/e, 2009.
2. Richard F. Gilberg, Behrouz A. Forouzan, Data Structures: A Pseudocode Approach with C,
2/e, Cengage Learning, 2005.
References
1. Horwitz E., S. Sahni and S. Anderson, Fundamentals of Data Structures in C, University Press
(India), 2008.
2. Aho A. V., J. E. Hopcroft and J. D. Ullman, Data Structures and Algorithms, Pearson
Publication,1983.
3. Tremblay J. P. and P. G. Sorenson, Introduction to Data Structures with Applications, Tata
McGraw Hill, 1995.
4. Peter Brass, Advanced Data Structures, Cambridge University Press, 2008
5. Lipschuts S., Theory and Problems of Data Structures, Schaum’s Series, 1986.
6. Wirth N., Algorithms + Data Structures = Programs, Prentice Hall, 2004.
7. Hugges J. K. and J. I. Michtm, A Structured Approach to Programming, PHI, 1987.
8. Martin Barrett, Clifford Wagner, And Unix: Tools For Software Design, John Wiley, 2008
reprint.
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COURSE PLAN
Sem.
Module Contents Hours
Exam
(56)
Marks
Introduction to programming methodologies – structured
approach, stepwise refinement techniques, programming style,
documentation – analysis of algorithms: frequency count,
I 9 15%
definition of Big O notation, asymptotic analysis of simple
algorithms. Recursive and iterative algorithms.
Abstract and Concrete Data Structures- Basic data structures –

vectors and arrays. Applications, Linked lists:- singly linked
list, doubly linked list, Circular linked list, operations on
II 9 15%
linked list, linked list with header nodes, applications of linked
list: polynomials,.
Applications of linked list (continued): Memory management,

memory allocation and de-allocation. First-fit, best-fit and
worst-fit allocation schemes
III 9 15%
Implementation of Stacks and Queues using arrays and linked
list, DEQUEUE (double ended queue). Multiple Stacks and
Queues, Applications.
String: - representation of strings, concatenation, substring

searching and deletion.
Trees: - m-ary Tree, Binary Trees – level and height of the

IV tree, complete-binary tree representation using array, tree 10 15%
traversals (Recursive and non-recursive), applications. Binary
search tree – creation, insertion and deletion and search
operations, applications.
Graphs – representation of graphs, BFS and DFS (analysis not

required) applications.
Sorting techniques – Bubble sort, Selection Sort, Insertion sort,

V 09 20%
Merge sort, Quick sort, Heaps and Heap sort. Searching
algorithms (Performance comparison expected. Detailed
analysis not required)
Linear and Binary search. (Performance comparison expected.

Detailed analysis not required)
Hash Tables – Hashing functions – Mid square, division,
VI 10 20%
folding, digit analysis, collusion resolution and Overflow
handling techniques.
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Question Paper Pattern:
1. There will be five parts in the question paper – A, B, C, D, E

2. Part A
a. Total marks : 12
b. Four questions each having 3 marks, uniformly covering module I and II; All
four questions have to be answered.
3. Part B
a. Total marks : 18
b. Three questions each having 9 marks, uniformly covering module I and II;
Two questions have to be answered. Each question can have a maximum of
three subparts
4. Part C
a. Total marks : 12
b. Four questions each having 3 marks, uniformly covering module III and IV;
All four questions have to be answered.
5. Part D
a. Total marks : 18
b. Three questions each having 9 marks, uniformly covering module III and IV;
Two questions have to be answered. Each question can have a maximum of
three subparts
6. Part E
a. Total Marks: 40
b. Six questions each carrying 10 marks, uniformly covering modules V and VI;
four questions have to be answered.
c. A question can have a maximum of three sub-parts.
7. There should be at least 60% analytical/numerical/design questions.
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www.ktustudents.

Course code Course Name L-T-P-Credits Year of

Abstract and Concrete Data Structures- Basic data structures –

Applications of linked list (continued): Memory management,

String: - representation of strings, concatenation, substring

Trees: - m-ary Tree, Binary Trees – level and height of the

Graphs – representation of graphs, BFS and DFS (analysis not

Sorting techniques – Bubble sort, Selection Sort, Insertion sort,

Linear and Binary search. (Performance comparison expected.

Question Paper Pattern:

1. There will be five parts in the question paper – A, B, C, D, E

7. There should be at least 60% analytical/numerical/design questions.

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