DATA STRUCTURES

BY :Coding Angel(AK)

💛 what is data structures? by Abhi Varshini 💙

A data structure is a specialized format for organizing and storing data. General data structure types include the array, the file, the record, the table, the tree, and so on. ... In computer programming, a data structure may be selected or designed to store data for the purpose of working on it with various algorithms.

1.Coding Angel❤️ concept: QUEUE

A queue is pretty much what it sounds like. It's a data structure that allows you to work through particular tasks (programs, steps, functions etc.) by ordering them and taking the first item in the queue and running it followed by the next item and so on. Thus a queue is a FIFO data structure i.e First in first out.

2. Sneha Chavan💙 concept: STACK

push n pop is very easy to understand..... once if you understand the concept you can easily write code for that.... you can understand it by 👉 first in last out👈first push data can pop last only.... stack is simply means storage (in cylindrical shape bin).. you can store(#push) data into it until its capabilities... once it full n then also you are passing data into it it becomes #overloaded, and have possibility of data lost.you can take out data this procedure known as #popyou can pop data until data remains in the bin if data is not available in bin, you simply cannot take any datayou can take place both push and pop simultaneously....

3.H Chiang💚 Concept :Array

What is an array, if not just an defined amount of chunk of memory set aside. Hence we can easily access it via index. Hashmap discussed in Java tutorial as a DataStructure similar to an array but bigger. It stores and retrieves via hashing a key, therefore it is quick. But it needs a lot more space than it needs because of collisions (keys with really close Hashes). Hence it is a bad choice if memory is at a premium. Another thing is, if memory had to be dynamically allocated, they will not be next to each other. Hence cost of going down the list. goes up but allocation costs goes “down” via linkable objects. They can create stacks, queques, binary trees, graphs.... However to truly appreciate DataStructures, we need some Discrete Math. It helps us choose the best data structure and way (program/algorithm) to use it.

4. Mohamed💚 concept :Graph

Another data structure to consider is a graph. Graphs are defined as follows: mathematical abstractions, consisting of vertices/nodes and edges/arcs. Edges connect two vertices. The two types of graphs are connected and disconnected. Connected means you can find a path between any two nodes, even if it is indirect. Disconnected means that you cannot find a path between any two nodes. A complete graph is a special type of connected graph, in which each possible pair of nodes have a connection to each other. Regarding edges, there are two types: directed and bidirectional. Directed graphs mean you can only go one way from one node to another. Bidirectional means you can go from node A to node B or node B to node A without the order mattering. Edges can also have weights, which are further explained below. Graphs are really useful in real world applications for map and travel related things. One of the most famous problems is "The Traveling Salesman", in which a salesman travels between multiple cities, or nodes, and there is a distance (or any other attribute we want to determine efficiency, such as travel cost or time between cities, depending on what we're looking for) associated with each pair of cities, which is the edge weight. Dijkstra's Algorithm is used to determine what route the salesman can take to be the most efficient. Back to computer science, we can use graphs that have vertices representing indices or references, and there are algorithms that can be written for traversing the graph to find what you're looking for.

5. Trees

There is also a similar data structure called Trees, which are similar to graphs, although they do not allow for loops or circuits in the tree. Trees are basically connected but not complete graphs. In other words, the edges branch out. Minimum spanning trees use the fewest possible edges to connect all of the nodes. Real world applications for this include electrical and computer engineering, where minimum spanning trees can be superimposed on an electrical circuit to derive Kirchoff Voltage and Current loops with the fewest possible dependent variables, making solving easier. As for computer science applications, an example would include determining if two separate programs need access to the same file at the same time based on a corresponding graph or tree. Discrete mathematics are highly useful in this field of study, and it combines the concepts of computer science, computer and electrical engineering, calculus, and differential equations. Further readings: Dijkstra's Algorithm https://en.m.wikipedia.org/wiki/Dijkstra%27s_algorithm Graphs https://en.m.wikipedia.org/wiki/Graph_(abstract_data_type)

6.💚Concept :Huffman codes (Aplication of trees)

Another application for trees is Huffman Codes, which are a method for data compression. You convert characters into strings of 1's and 0's based on the tree, taking into account the most frequently used characters, and give those the shortest strings (so, if A and E are popular, give them 2 bit strings, instead of making each letter in the alphabet 5 bits, for example). The way to ensure you don't have confusion over whether you are starting a new character string is to use Prefix Codes (which is another name for Huffman Codes), and, counterintuitively, it means no letter's code can be the beginning of another. This is where the tree comes into play. For example, if B is 001, then A cannot be 00 as the decoder wouldn't know if 00 is a whole character or the beginning of B.

Notes By
1.Coding Angel (myself)
2.Mohamed
3. Sneha Chavan
4.H Chiang
5.Abhi Varshini
6. Divesh Singh(Code BST)
sorry for the spelling mistakes.

💛 DataStructure