The document provides an overview of data structures and algorithms, explaining basic terminology and concepts essential for efficient programming. It discusses the classification of data structures into primitive, non-primitive, linear, and non-linear types, as well as operations such as traversing, searching, and sorting. Additionally, it presents examples of algorithms, emphasizing the significance of efficient data management and the role of abstract data types.

1. Elementary of
Data Structure &
Algorithms
B. MANDAL

2. Basic Terminology
A good program is that
 Runs correctly
 Easy to read & understand
 Easy to debug
 Easy to modify
A program should undoubtedly
 Gives correct result &
 Efficiently
To write efficient programs – we need to
enforce certain data management concepts

3.  A group of data elements that put together under one
name.
 Data Structure is a way of storing and organizing
data in computer system.
 Logically &
 Mathematically
 Purpose :– For efficient access of stored data
Data Structure

4.  Data: a value or set of values
 Record: collection of data items
 E.g. name, address & subject for a particular student
 File: collection of related records [Dictionary]
 Key: one or more data item(s) which will uniquely
identify each records form a file.
Data Structure – Elementary

5.  Primitive: atomic, fundamental data types (e.g. integer,
real, Boolean)
 Non-primitive: derived from primitive DS (e.g. array,
structure)
 Linear: elements are stored in linear or sequential manner.
 Linear memory allocations
 Linear relationship between elements
 E.g. Array, linked list, Stack, Queue …
 Non-Linear: elements are not stored in linear manner.
 E.g. Graph, Tree
Classification of Data Structure

6.  Traversing: Accessing each data item exactly once
 Searching: Finding location of one/more data items
 Inserting: Add new data items
 Deleting: Removing data
 Sorting: Arranging data
 Merging: Combining set of data
Operations on Data Structure

7.  Way we look at a Data Structure
 Focusing on what it dose
 Ignoring implementation details
 E.g. List, Stack, Queue …
 Real world problem solving
Abstract Data Type (ADT)

8.  Any well-defined computational procedure
 Takes some value, or set of values, as input
 produces some value, or set of values, as output
An algorithm is a sequence of computational steps that
transform the input into the output.
 For a particular problem – we may have several
algorithms
 One algorithm may not fit for other problems
Algorithm

9.  Ex. Searching an element from a list
Algo: Search(A, key, N)
{
For i=0 to N
if( A[i] == key )
RETURN (i)
RETURN (-1)
}

10.  Ex. GCD of two number
Al go rith m: G CD(a, b)
{
wh il e (a ≠ b)
{
if a > b
a ← a – b
el se
b ← b – a
}
R ETU R N (a)
}

11.  Ex. Factorial of positive integer
Algorithm: Factorial(n)
{
Fact ← 1
If n = 1
return (1)
else
Fact ← n * Factorial(n - 1)
return (Fact)
}

12.  Collection of similar data item
 Referred by a common name
 Contiguous memory allocation
 E.g. int Student[10];
Array

13. 2-Dimentional Array
 E.g. char matrix[5] [4];


14.  E.g. char matrix[6] [12];
 Will not fit


15.  2-dimensional array mapped to 1-dimensional array
 Special way to access
 E.g.
char A[3][4];
A ⇒ 0 1 2 3
0
1
2
A´ ⇒
Representation in Memory
a b c d
e f g h
i j k l
a b c d e f g h i j k l
0 1 2 3 4 5 6 7 8 9 10 11
A [row][col] = A´ [row * m + col]
m  # columns

16. Dynamic Memory Allocation
 Calloc – contiguous memory allocation
 Arr = (int *) calloc(n, sizeof(int))
 Malloc – block of memory allocation
 Arr = (int *) malloc(n * sizeof(int))
 Free: release memory
 free(Arr)
 **Arr is a pointer to integer

17. Thank You 