Course Code: AI203

Course Name: Data Structures &

Algorithms

Class : S.Y. B. Tech Sem : III

Mrs. Pragati P. Patil

Assistant Professor
Department of CSE(AI&ML)
 Evaluation Scheme
Course Course Teaching Scheme Evaluation Scheme
Code L T P Theory (Marks Practical
%) (Marks
%)

Scheme
Credits
Mi
n.
Min. for Ma for
Max
passing x. pas
sin
ISE 20 --- g---
Data
structure & UT1 15 4 --- ---

Algorithms 0
AI203 UT2 15 4 --- ---
3 - - 3
0

ESE 50 40 --- ---

 Course Learning Outcomes
After successful completion of the course, strudels will be able to:
1. Compare between linear and nonlinear data structures.
2. Describe the characteristics of various data structure such
as stacks, queues, trees, graphs and Hash tables.
3. Analyze various searching and sorting algorithms and apply
it to solve particular problem.
4. Determine a suitable data structure and algorithm to solve
a real world problem.
 Syllabus
• Unit – 1 : Introduction to Data Structures
• Primitive and non-primitive data structures, Operations on data structures,
Algorithms, Abstract Data Types, Complexity Analysis.
• Unit - 2 : Linear Data Structures
• Stack: Definition, Representation and Applications of Stack.
• Queue: Definitions, Representation and Applications of Linear Queue, Circular
Queue, and Priority Queue.
• Unit – 3 : Linked Lists
• Definition, Representation, Operations and Applications of singly linked list,
doubly linked list, circular linked list, Application of linked list-Stack & queue,
Introduction to Sparse matrix, representation of sparse matrix using linked list.
 Syllabus
• Unit – 4 : Searching, Sorting and Hashing Techniques
• Linear search, Binary search, Bubble sort, insertion sort, Merge sort, Quick
sort, Selection sort, Radix sort, Heap sort, Complexity of algorithms,
• Hashing: Definition, Hash functions, Overflow, Collision, Open Hashing,
closed hashing, Rehashing Techniques.
• Unit - 5 : Trees
• Basic Technology, Binary Tree, Traversal methods, Binary search tree, AVL
Tree, B tree, B+ tree, Heaps - operations and their applications.
• Unit – 6 : Graphs
• Basic concepts of graph theory, Storage representation, Operations on
graphs, Traversing a graph, Shortest path algorithm.
 Books
• Text Books:
• Data structures -- Seymour Lipschutz (MGH) Schaum’s Outlines.

• Reference Books:
• Data structures and Algorithms -- Alfred V. Aho, John E. Hopcroft,
J. D. Ullman (Addision- Wesely Series)
• Introduction to Data Structures in C – Ashok N. Kamthane
(Pearson Education).
 Unit – I : Introduction to Data
Structures
• Primitive and non-primitive data structures,
• Operations on data structures, Algorithms,
• Abstract Data Types,
• Complexity Analysis
 What is Data?

• Data : Data is a collection of facts and

figures or a set of values or values of a
specific format that refers to a single set of
item values.
• Structure: Structure refers to how data is
structured. It specifies the relationship
between various data elements and how
they are kept in memory. The structure has
an impact on the efficiency of data
operations.
 What is Data Structure?
• Data Structure is a method of managing and arranging data in
such a way that we can conduct operations on these data in an
efficient way.
• Data Structures is about providing or contributing data elements
for better organization and storage.
• A data structure is a particular way of organizing data in a
computer so that it can be used effectively.
• The idea is to reduce the space and time complexities of different
tasks.
 Why we should learn Data
Structures?
• Problem Solving.
• Algorithm Design.
• Better Understanding of Computer Science.
• Requirements for the Job.
• Competitive Programming.
 What is the need of Data Structure?
• Improved Time Complexity:
• For example, searching for an element in a sorted array is faster than searching for it in
an unsorted array.
• Better Space Complexity:
• For example, using a linked list instead of an array can reduce the amount of memory
needed to store the same data.
• Efficient Data Retrieval:
• For example, a hash table can retrieve data in constant time, while searching through an
unsorted array takes linear time.
• Better Data Management:
• For example, a stack can be used to implement an undo functionality in an application.
• Solving Complex Problems:.
• For example, graph algorithms can be used to find the shortest path between two points.
 Characteristics of Data Structure
• Representation of Data: Data structures define a way of representing data in a
computer's memory, making it possible to store, manipulate, and access data efficiently.
• Access Techniques: Different data structures provide different techniques for accessing
data stored within them, such as random access or sequential access.
• Storage Organization: Data structures define the organization of data in memory, such
as linear or hierarchical organization.
• Insertion and Deletion Operations: Different data structures support different
methods for adding and removing elements, such as insertion at the end or deletion from
the front.
• Time and Space Complexity: Data structures can have different time and space
complexities, depending on the operations they support and the way they organize data.