This document provides an overview and agenda for a course on data structures and algorithms. The course objectives are to understand the concepts and costs/benefits of commonly used data structures, how to select appropriate structures based on requirements, and implement structures in code. The agenda covers introduction to structures like linked lists, stacks, queues, trees and graphs as well as sorting algorithms. It also discusses analyzing algorithm efficiency and the types and methodologies for selecting optimal data structures.

1. Data Structures and Algorithms
An Introduction

2. Course Objectives
 Understanding the concepts of cost and benefits for data
structures
 Understand commonly used data structures
 Understand the effectiveness of data structure
 Select a data structure for specific requirement
 Implement data structures in a programming language

3. Agenda
 Introduction to Data Structures
 Importance and use of Data Structures
 Linked Lists
 Stack
 Queue
 Overview of Trees
 Overview of Graphs
 Sorting Algorithms

4. Session Agenda
 Need of Data Structures
 Data Structures Benefits
 Data Structures Terminologies
 Methodologies for Analyzing Algorithm
 Asymptotic Notations
 Types of Data Structures
 Linear
 Non Linear

5. Session Agenda
 Arrays
 Pointers
 Structures
 Linear Linked Lists
 Singly Linked Lists
 Doubly Linked Lists
 Circular Linked Lists ( Singly and Doubly)

6. Need for Data Structure
 Applications are Complex
 Complex Applications demand more Computations
 Computations require Complex Data
 Complex Data require Complex organization
 Complex Organizations require Complex Processing
(Searching, Insertion, Deletion)
 Data structures organize data
 Provides way of storing data so that it can be used efficiently

7. Selection of Data Structures
 The cost of a solution is the amount of resources
 The choice of data structure and algorithm can make the
difference in execution of a program
 Carefully chosen data structure will allow the most effective
algorithm to be used
 A well-designed data structure allows a variety of critical
operations to be performed, using few resources
 Quality and performance of the final result depends heavily
on choosing the best data structure

8. How to Select a data structure
 Analyze the problem to determine the resource constraints a
solution must meet
 Determine the basic operations that must be supported
 Quantify the resource constraints for each operation
 Select the data structure that best meets these
requirements
 Some questions to ask
 Amount of data to store
 Are all data inserted into the data structure at the beginning,
or are insertions interspersed with other operations?
 Can data be deleted? If so, a more complex representation is
typically required
 Are all data processed in some well-defined order, or is random
access allowed?

9. Data Structure Philosophy
 Each data structure has costs and benefits
 Rarely is one data structure better than another in all
situations.
 A data structure requires:
 space for each data item it stores
 time to perform each basic operation
 programming efforts
 Each problem has constraints on available space and time
 Only after a careful analysis of problem characteristics can
we know the best data structure for the task

10. Methodologies for Analyzing Algorithm
 How much of a computer time and memory is utilized by an
algorithm
 Methodologies
 Empirical Comparison (run programs)
 Asymptotic Algorithm Analysis
 The efficiency analysis concentrates on critical operations:
 data interchanges (swaps)
 comparisons (<, >, ==, ! =)
 arithmetic operations (+, -, =)
 Factors affecting running time
 Critical resources
 Size of the input
 Machine load
 Operating System, Compiler, …
 Problem size

11. Types of Data Structures
 Base Data Structures
 Primitive
 Composite
 Linear Data Structures
 Array
 Linked List
 Non-Linear Data Structures
 Graph
 Tree

12. Arrays
 Arrays are the most common data structure used to store
collections of elements
 Access any element using its index number
 Occupies contiguous memory area
 Example:
int scores[100];
scores[0] = 18;
scores[1] = 5;
scores[2] = 24;
scores
18 5 24 -2134 ... ... ... 14217
Index 0 1 2 3 . . . . . . . 99

13. Pointers
 A variable capable of storing an address is called a pointer
variable.
 Consider the variable declaration:
int *ptr;
 Such a pointer is said to "point to" an integer.
 Storing in ptr the address of an integer variable k is done as
ptr = &k;
 The "dereferencing operator" refers to the value of that
which ptr is pointing to.
*ptr = 7;
 To print to the screen the integer value stored at the address
pointed to by ptr.
printf("%dn",*ptr);

14. Structures
 We can declare the form of a block of data containing
different data types by means of a structure declaration.
 For example,
struct person
{
char name[20];
int age;
float weight;
};
 We now declare a variable of type person
struct person p;
 and access the structure data using this variable as
p.age

15. Structures Continued…
 We can also declare a pointer to a structure with the
declaration:
struct person *ptr;
 and we point it to our example structure with:
st_ptr = &p;
 Now, we can access a given member by de-referencing the
pointer as
(*st_ptr).age = 63;
 Or use an alternative syntax as
st_ptr->age = 63;