A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.
What is data structure example?
Data Structure can be defined as the group of data elements that provides an efficient way of storing and organizing data in the computer so that it can be used efficiently. Some examples of Data Structures are arrays, Linked List, Stack, Queue, etc.
Which language is best for data structures?
C++ is the best language for not only competitive but also used to solve algorithm and data structure problems. C++ use increases the computational level of thinking in memory, time complexity, and data flow level.
What is data algorithm?
An 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. Search − Algorithm to search an item in a data structure. Sort − Algorithm to sort items in a certain order. Insert − Algorithm to insert an item in a data structure.
When we think of data structures, there are generally four forms:
- Linear: arrays, lists
- Tree: binary, heaps, space partitioning etc.
- Hash: distributed hash table, hash tree etc.
- Graphs: decision, directed, acyclic etc.
Why to Learn Data Structure and Algorithms?
As applications are getting complex and data-rich, there are three common problems that applications face nowadays.
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, the 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.
Applications of Data Structure and Algorithms
An 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.
From the data structure point of view, the 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 an 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 −
An array is a finite group of data, which is allocated contiguous (i.e. sharing a common border) memory locations, and each element within the array is accessed via an index key (typically numerical, and zero-based).
A linked list is different from an array in that the order of the elements within the list is not determined by a contiguous memory allocation. Instead, the elements of the linked list can be sporadically placed in memory due to its design, which is that each element of the list (also referred to as a ‘node’) consists of two parts: the data, a pointer
The concept of a ‘tree’ in its simplest terms is to represent a hierarchical tree structure, with a root value and subtrees of children (with a parent node), represented as a set of linked nodes.
There is various types of the basic tree structure, each with its own unique characteristics and performance considerations:
- Binary Tree
- Binary Search Tree
- Red-Black Tree
- Weight-balanced Tree
- Abstract Syntax Tree
A hash table is a data structure that is capable of mapping ‘keys’ to ‘values, and you’ll typically find this is abstracted and enhanced with additional behaviors by many high-level programming languages such that they behave like an ‘associative array’ abstract data type.
A graph is an abstract data type intended to guide the implementation of a data structure following the principles of graph theory.
Happy Algorithm 🙂