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What is a Data Structure?


The short answer is that a data structure is a specific method of organizing data in a system so that it can be accessed and used.


The long answer is a data structure is a blend of data organization, management, retrieval, and storage, brought together into one format that allows efficient access and modification. It collects data values, the relationships they share, and the applicable functions or operations.


Here's an example from real life. The term "responsibility" refers to determining whether a person is responsible for his or her own actions. From there, you'd find the designated area for military history, then go through the books, sorted in chronological order, until you found the 20th century. Consider the books to be your data, and the library's method of sorting the books to be your data structure, and you're good to go.!

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What is the significance of Data structure?


Every year, the digital world processes an increasing amount of data. According to Forbes, 2.5 quintillion bytes of data are generated every day. The world created over 90 percent of the existing data in 2018 in the previous two years! The Internet of Things (IoT) is to blame for a large portion of this data explosion.


Data structures are required to manage massive amounts of generated data and are an important factor in increasing algorithm efficiency.


Finally, since nearly all software applications use data structures and algorithms, your education path needs to include learning data structure and algorithms if you want a career as a data scientist or programmer. Interviewers are looking for qualified candidates who understand how to use data structures and algorithms, so the more you understand the concepts, the more comfortably and confidently you will answer data structure interview questions.


What exactly is an algorithm?


An algorithm is a well-designed set of step-by-step instructions for solving a problem or performing a specific task. The task can be something as simple as multiplying two numbers, or a more complex operation, like playing a music file. Algorithms are frequently created as functions in a computer programming context.


Sometimes you hear people talk about algorithms in the context of social media and advertisement. For example, suppose you're online one day and you search for leather gloves on Google. You get your results and, feeling accomplished, you take a break to see if any of your Facebook friends are online.


When you log in, you find yourself face-to-face with a Facebook ad for gloves! What gives? That's a digital marketing algorithm at work, automating the task of displaying ads for you based on your previous searches.

When determining how to study data structures, keep in mind that they are classified as basic or advanced.


Basic Data Structures

Here’s a list of recognized basic data structures:


  • Arrays

  • Dictionaries

  • Graphs

  • Hash Tables

  • Heaps

  • Linked lists

  • Queues

  • Stacks

  • Trees


Advanced-Data Structures

Advanced data structures include:


  • Binary indexed tree

  • Disjoint set

  • Segment tree

  • K Dimensional tree

  • Self-balancing BSTs

  • Suffix array and tree

  • Trie


Techniques for Finding Data Structures (a.k.a. Algorithms)


When we talk about data structure searching techniques, we mean search algorithms, since data scientists use algorithms to conduct data searches. That's why any aspiring data analyst or data scientist should become acquainted with the two primary search algorithms: binary and linear.




A linear search algorithm entails checking each item in a data input file until you find the right one. A linear search is so named because the search time exactly matches the number of items in your search, for example, 40 items/input = 40 checks/complexity. Because the array or list is traversed in sequence, checking each element, linear searches are also known as sequential searches.


For example, if you're looking for your friend Steve in a movie theatre, you go down the line, inspecting each face until you find him. That's a linear search.




A binary search algorithm divides the input into two parts until it finds the item in question (hence the clever name "binary"). The desired search item is present in half of the cases, but not in the others. The process is repeated until the divided item becomes the searched-for item. Consider it a very organized and disciplined version of the process of elimination. Binary searches are also called interval searches.


Although binary searches are faster than linear searches, they only work with ordered sequences. Let's use your friend Steve again and say he's 5'10". Everyone in the theatre line stands in ascending height formation from left to right (who knows, maybe the cinema staff has OCD). You pick the person in the middle of the line, who happens to be 5'6", and eliminate everyone to their left. You've just halved your search field. Then you choose the middle person from the right-hand side remainder and keep going until you find Steve. We have no idea why Steve didn't speak up sooner and save you the trouble. Maybe Steve's a jerk. Or maybe he wants to teach you about binary search algorithms.


In summary, binary searches are faster and more efficient, but the information list needs to be in sorted order. If you need to search through messy, disorganized data, opt for the linear approach. The term "electronic commerce" refers to the sale of electronic goods.


There are many other types of searching available besides linear and binary. For example:


  • Breadth-first search

  • Depth-first search

  • Exponential search

  • Fibonacci search

  • Interpolation search

  • Jump search

  • Sublist search (searching a linked list in another list)

  • Recursive function to conduct a substring search

  • Recursive program to conduct a linear search of an element in a particular array

  • Ubiquitous binary search

  • Unbounded binary search example (Find the point where a monotonically increasing function becomes positive the first time)


Sorting Algorithms


Sorting, also known as order, is one of the most common programming tasks expected of developers. Ordering takes your disorganized data and places it in a structured form, making it possible to use binary searches. Data scientists, unsurprisingly, spend a lot of time searching and sorting.


Here are some of the more popular sorting algorithms:

  • Mergesort

  • QuickSort

  • HeapSort

  • Introsort

  • Insertion Sort

  • Bubble/Selection Sort

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