Data Structures are the programmatic way of storing data so that data can be used efficiently.
Almost every enterprise application uses various types of data structures in one or the other way.
This tutorial will give you a great understanding on Data Structures needed to understand the
complexity of enterprise level applications and need of algorithms, and data structures.
Why to Learn Data Structure and Algorithms?
As applications are getting complex and data rich, there are three common problems that
applications face now-a-days.
Data Search − Consider an inventory of 1 million(106) items of a store. If the application
is to search an item, it has to search an item in 1 million(106) items every time slowing
down the search. As data grows, search will become slower.
Processor speed − Processor speed although being very high, falls limited if the data
grows to billion records.
Multiple requests − As thousands of users can search data simultaneously on a web
server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to rescue. Data can be organized
in a data structure in such a way that all items may not be required to be searched, and the
required data can be searched almost instantly.
Applications of Data Structure and Algorithms
Algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a
certain order to get the desired output. Algorithms are generally created independent of
underlying languages, i.e. an algorithm can be implemented in more than one programming
language.
From the data structure point of view, following are some important categories of algorithms −
Search − Algorithm to search an item in a data structure.
Sort − Algorithm to sort items in a certain order.
Insert − Algorithm to insert item in a data structure.
Update − Algorithm to update an existing item in a data structure.
Delete − Algorithm to delete an existing item from a data structure.
The following computer problems can be solved using Data Structures −
Fibonacci number series
Knapsack problem
Tower of Hanoi
All pair shortest path by Floyd-Warshall
Shortest path by Dijkstra
Project scheduling
, Data Structures & Algorithms - Overview
Data Structure is a systematic way to organize data in order to use it efficiently. Following terms
are the foundation terms of a data structure.
Interface − Each data structure has an interface. Interface represents the set of operations
that a data structure supports. An interface only provides the list of supported operations,
type of parameters they can accept and return type of these operations.
Implementation − Implementation provides the internal representation of a data
structure. Implementation also provides the definition of the algorithms used in the
operations of the data structure.
Characteristics of a Data Structure
Correctness − Data structure implementation should implement its interface correctly.
Time Complexity − Running time or the execution time of operations of data structure
must be as small as possible.
Space Complexity − Memory usage of a data structure operation should be as little as
possible.
Need for Data Structure
As applications are getting complex and data rich, there are three common problems that
applications face now-a-days.
Data Search − Consider an inventory of 1 million(106) items of a store. If the application
is to search an item, it has to search an item in 1 million(106) items every time slowing
down the search. As data grows, search will become slower.
Processor speed − Processor speed although being very high, falls limited if the data
grows to billion records.
Multiple requests − As thousands of users can search data simultaneously on a web
server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to rescue. Data can be organized
in a data structure in such a way that all items may not be required to be searched, and the
required data can be searched almost instantly.
Execution Time Cases
There are three cases which are usually used to compare various data structure's execution time
in a relative manner.
Almost every enterprise application uses various types of data structures in one or the other way.
This tutorial will give you a great understanding on Data Structures needed to understand the
complexity of enterprise level applications and need of algorithms, and data structures.
Why to Learn Data Structure and Algorithms?
As applications are getting complex and data rich, there are three common problems that
applications face now-a-days.
Data Search − Consider an inventory of 1 million(106) items of a store. If the application
is to search an item, it has to search an item in 1 million(106) items every time slowing
down the search. As data grows, search will become slower.
Processor speed − Processor speed although being very high, falls limited if the data
grows to billion records.
Multiple requests − As thousands of users can search data simultaneously on a web
server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to rescue. Data can be organized
in a data structure in such a way that all items may not be required to be searched, and the
required data can be searched almost instantly.
Applications of Data Structure and Algorithms
Algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a
certain order to get the desired output. Algorithms are generally created independent of
underlying languages, i.e. an algorithm can be implemented in more than one programming
language.
From the data structure point of view, following are some important categories of algorithms −
Search − Algorithm to search an item in a data structure.
Sort − Algorithm to sort items in a certain order.
Insert − Algorithm to insert item in a data structure.
Update − Algorithm to update an existing item in a data structure.
Delete − Algorithm to delete an existing item from a data structure.
The following computer problems can be solved using Data Structures −
Fibonacci number series
Knapsack problem
Tower of Hanoi
All pair shortest path by Floyd-Warshall
Shortest path by Dijkstra
Project scheduling
, Data Structures & Algorithms - Overview
Data Structure is a systematic way to organize data in order to use it efficiently. Following terms
are the foundation terms of a data structure.
Interface − Each data structure has an interface. Interface represents the set of operations
that a data structure supports. An interface only provides the list of supported operations,
type of parameters they can accept and return type of these operations.
Implementation − Implementation provides the internal representation of a data
structure. Implementation also provides the definition of the algorithms used in the
operations of the data structure.
Characteristics of a Data Structure
Correctness − Data structure implementation should implement its interface correctly.
Time Complexity − Running time or the execution time of operations of data structure
must be as small as possible.
Space Complexity − Memory usage of a data structure operation should be as little as
possible.
Need for Data Structure
As applications are getting complex and data rich, there are three common problems that
applications face now-a-days.
Data Search − Consider an inventory of 1 million(106) items of a store. If the application
is to search an item, it has to search an item in 1 million(106) items every time slowing
down the search. As data grows, search will become slower.
Processor speed − Processor speed although being very high, falls limited if the data
grows to billion records.
Multiple requests − As thousands of users can search data simultaneously on a web
server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to rescue. Data can be organized
in a data structure in such a way that all items may not be required to be searched, and the
required data can be searched almost instantly.
Execution Time Cases
There are three cases which are usually used to compare various data structure's execution time
in a relative manner.
