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Data Structures


Data structure refer to the organizing and collecting the data to perform an effective way. It is a logical way of storing the data and also define the mechanism of the retrieve data. It is a way to organizing the data and data can be easier to use. Data structure are the method of representing the logical relationships between the individual data elements and solution to the given problems. Data structure are categorized into two main parts such as- abstract data types and primitive data types. Abstract datatypes define the operation to be performed on data structure and primitive data types define how to extract the data to control the flow of processing algorithms. 


Data structure have different Efficiency and Complexity - Time versus space complexity, Worst versus average complexity, Concrete measures for performance, Big-O notation for complexity class, Formal definition of complexity classes . .

Data structure are divided into two different types :-

1- Built-in data structure includes integer, float, character and pointer.
2- User-defined data structure includes arrays, list(linear list(stack and queues) and non-linear list(trees and graphs)) and files.


Important subtopics related to data structure are-

- Array
- Linked List
- Stack
- Queue
- Binary Tree
- Binary Search Tree
- Heap
- Hashing
- Graph
- Matrix
- Advanced Data Structure


A data structure is a scheme for organizing and storing data in the memory. Some of the most commonly used data structures are as follows lists, arrays, queues, stacks, trees and graphs.


The way in which the data is organized also affects the performance of a program for different tasks. The most important task of the Computer programmers decide which data structures to use based on the nature of the data and where it is being used.


A queue is a commonly used example of simple data structure. A queue has beginning and an end, called the front and back of the queue.


Data enter in the queue at one end and leaves at the other end and data exits the queue in the same order in which it enters the queue, like people in a checkout line at a supermarket.


A binary tree is another commonly used data structure. It is organized like an up-down tree. Each child on the tree is term as node, holds an item of data along with a left pointer and a right pointer.


The pointers are lined up in the way so that the structure forms the upside down tree, with a single node at the top, called the root node, and branches increasing on the left and right as you go down the tree.


A list is an ordered set of data. It is basically used to store objects that are processed sequentially.


An array is an indexed set of variables, such as movie [1], movie [2], and movie [3]. It is like a set of variable that hold things.


Data Structure is a procedure of assembling and organizing the data in such a manner that it can perform the operations on the data in an impressive manner. Data Structures is defined as the rendering of data in terms of the relationship, for storage and better organization.

In simple word, Data Structures is a structures programmed that is used to store the data in a ordered way, so that the operations can be executed easily. Some complicated Data Structures that are used to store connected and large data. Abstract Data Structure examples are:


  • Tree
  • Linked List
  • Stack
  • Graph
  • Queue etc.


All these abstract data structures allow performing various operations on data. These data structures can be selected on the basis of type of operation required. Anything which can perform the storage of data, is called data structure, So Float, Integer, Char, Boolean, etc, are data structures. They are called as Primitive Data Structures. An algorithm is defined as a finite set of logic or instructions that are written in order to perform a predefined task. Algorithm is nothing but it is just a core solution of a problem, that can be described either by using a flowchart or as pseudocode .


Programmatic way for storing data is known as data structures and this is done for using data efficiently and this is achieved by using different data structures according to the organization needs. A step by step process or procedure for defining a set of instructions which are needed to be executed to obtain a desired output is known as algorithm. Algorithms can be created independently without lying under any language and it can be implemented in any programming language.


For data structure, following categories of algorithms are used:

1. Search – for finding item in data structure.
2. Sort - sorting the data in a certain order.
3. Insert – inserting item into data structure.
4. Update – updating existing items in data structure.
5. Delete – deleting existing items from data structure.


In data structures, algorithms are needed to be unambiguous, input should be zero or well defined, output should be 1 or well defined, should terminate after finite steps,  should be feasible and there should be step by step direction which should not depend on any programming code.

There are no well defined standards for writing algorithms, it is a problem and depends on resource and these are not written for any particular programming code. Two types of  algorithm analysis are used for making algorithm efficient and these analyses are priori analysis and posterior analysis and these analyses are theoretical and empirical analysis  respectively. Algorithm complexity is determined by two factors and these factors are: Time factor and Space factor.


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Topics covered under Data Structures Assignment help :


  • Arrays , strings , string implementation alternatives , Recursive algorithms, programming
  • Dynamic storage allocation ,pointers,Lists , Exceptions , Linked Lists ,Big-O Notation, Complexity , Big-O Notation continued , Amortized run-time , Stacks and queues
  • Recursion , Binary Search Trees , AVL Trees , Priority Queues and Heaps, Hashing , Graphs , Silly sorts Sorting , Merge sort , Quick Sort , Selection


Help for More complicated Data Structures topics like :


  • Design well‐structured algorithms, Code algorithms in single‐ and multi‐file C++ programs , good coding standards
  • Compile, execute, test, debug, and informally verify correct operation of programs, Abstract data type (ADT), Object‐oriented concepts, encapsulation, information hiding, classes and methods, Design, implementation, and use of linear ADTs: arrays, stacks, queues, and lists,
  • Big‐O notation ,efficiency of some common algorithms, C++ classes, access rules, inheritance, friends, abstract classes, passing parameters by value, by reference, polymorphism, functions, operators, static, dynamic binding, templates, searching, sorting, pointer implementation lists, stacks, queues, trees, hashing, P, NP classes and analysis algorithms.


Data structures: arrays, lists, queues, stacks, and sets Trees: binary trees Recursion Traversing binary trees
Dictionary based data structures binary search trees,hash tables, maps Analysis of running time of algorithms Algorithms for sorting and searching
Elementary tree and graph algorithms Storing and Hash Tables Graphs depth first and breadth first search
Finite state machines automata formal and regular languages Turing machines