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What are Data Structures & Algorithm

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25th Dec, 2022
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What are Data Structures & Algorithm

A data structure organizes data in a virtual system. Its example can be sequences of numbers, data, or tables. Data Structures represent the programmatic method of storing data to ensure efficient usage. Most enterprise applications use different kinds of data structures.

An algorithm is a series of steps a computer executes by taking input and transforming it into a target output.  In other words, it is a step-by-step process that defines a set of instructions to be implemented in a specific order to obtain the desired output. Generally, algorithms are created independent of the underlying languages. It means that an algorithm can be executed in multiple programming languages.

Data structures and algorithms combine and help the programmers build different computer programs. A profound study into data structures and algorithms guarantees efficient and well-optimized code.

In computer science, all programs, software, and applications include two fundamental elements – (i) Data and (ii) Algorithms. The data is information, and the algorithms are sets of instructions that convert the raw data into valuable components for further programming. You can remember the following equations to avoid confusion:

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Set of related data + Set of allowed operations on the data = Data Structures

Data structures + Algorithms = Programs

The following sections give you an understanding of the reasons to learn Data Structure and Algorithms, how they work together, their applications, and standard Data Structure and Algorithms.

Let’s get started with the importance of data structures and their types:

Why Data Structure?

Understanding data structures enable you to comprehend and choose the appropriate one for your project and requirements. As a result, you can write time and memory-efficient code.

Types of Data Structure

Data structures are mainly divided into two categories:

1) Linear data structure

2) Non-linear data structure

1) Linear data structures

In these types of data structures, the elements are organized in sequence. Because the elements are arranged in a specific order, the implementation becomes easy.  However, with the increase in program complexity, linear data structures may not be the most suitable choice.

Prevalent linear data structures are:

  1. Array Data Structure
  2. Stack Data Structure
  3. Queue Data Structure
  4. Linked List Data Structure

1. Array Data Structure

In an array, all elements are organized in continuous memory, with all belonging to the same type. The programming language determines the elements’ type stored in the form of arrays. For example, if you need to store data sequentially in the memory, you can use the Array data structure.

2. Stack Data Structure

The elements are stored in the LIFO method. It means the last element stored in a stack would be removed first. Its working is identical to piles of plates in which the last plate placed on the pile will be discarded first.

3. Queue Data Structure

This data structure adopts the FIFO method, i.e., the first element stored in the queue will be taken away first. Its working is identical to a queue of students at the admission counter where the first student in the queue gets admission first.

4. Linked List Data Structure

The data elements are linked via a series of nodes. Every node includes the data items and addresses to the following node.

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2) Non-linear data structures

Unlike linear data structures, the elements present in non-linear data structures are not organized in a sequence. They are organized in a hierarchical method where one element will be linked to one or multiple elements.

The following list shows the classification of non-linear data structures:

  1. Graph Data Structure
  2. Trees Data Structure
  1. Graph Data Structure

In the graph data structure, every node is known as a vertex, and every vertex is linked to other vertices via edges.

The famous Graph Data Structures:

  • Strongly Connected Components
  • Spanning Tree and Minimum Spanning Tree
  • Adjacency List
  • Adjacency Matrix

 2. Trees Data Structure

Identical to a graph, a tree is an assortment of edges and vertices. But in this data structure, there can only be one edge between the two vertices.

The famous Tree based Data Structures:

  • Binary Search Tree
  • Binary Tree
  • B-Tree
  • B+ Tree
  • AVL Tree
  • Red-Black Tree

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Reasons to learn Data Structure and Algorithms

Whether it’s marketing, travel, or manufacturing, digitization supports programming. Programming is seen in all fields of applications, and all these applications demand expert IT professionals. Data structures and algorithms are the fundamental facets of any piece of computer code or program.

With the increase in complexity of the applications, the three common problems faced are:

  • Processor speed

Although the processor speed may be very high, it will be limited if the data volume increases to a billion records.

  • Data Search

As the data grows, the search becomes slower. For example, suppose a store has 1 million items. If the application demands searching an item, it will have to search it 1 million times every time, which slows down the data search process.

  • Multiple requests

Many users search data simultaneously on a web server, so even the quick server is sometimes inefficient during the data search process.

Data structures and algorithms are useful for solving these aforementioned problems. They organize data so that all the items are not required to be searched, and the targeted data can be instantly searched.

How do Data Structures and Algorithms work together?

Various algorithms are designed to accomplish different purposes. They interact with various data structures but with an identical computational complexity scale. The algorithms are considered as dynamic core pieces interacting with static data structures.

The data is flexibly expressed in code. Once you know how algorithms are developed and how a related family of languages works semantically, you can generalize them across various programming languages. When you go through the fundamentals of programming languages and their consolidating principles, you can easily switch between the various languages and learn them faster.

Commonly used Data Structures and Algorithms

The following list shows those data structures you will find across various programming languages:

  • Queues
  • Stacks
  • Linked lists
  • Maps
  • Sets
  • Search trees
  • Hash tables

Each of these data structures and algorithms has its unique computational complexity for related functions like adding items and calculating aggregate measures (for example, finding the mean for the underlying data structure).

Common categories of algorithms are

  • Sort – (sort items in a specific order)
  • Search (searches an item in a data structure)
  • Insert – (inserts item in a data structure)
  • Update (updates an existing item in a data structure)
  • Delete (deletes an existing item from a data structure)

Other categories of algorithms include

  • Dynamic programming
  • Graph/tree traversing
  • Hashing and regex (string pattern matching)

Applications of Data Structures and Algorithms

Data structures and algorithms help to solve the following types of computer problems:

  • Knapsack problem
  • Shortest path by Dijkstra
  • Fibonacci number series
  • All pair shortest path by Floyd-Warshall
  • Tower of Hanoi
  • Project scheduling

Data structures and algorithms are used in various applications in IT processes and as data structures and algorithms in python. Some of them are discussed here:

  • Data Storage:

Data structures support efficient data persistence, including recognizing indicator collections and listing according to the corresponding structures. Therefore, data structures and algorithms are quite useful in database management systems for storing records.

  • Data Exchange:

The organized information gets easily distributed between various applications, including TCP/IP packets.

  • Scalability:

Big data applications immensely depend on data structures and algorithms for data storage over distributed storage locations. Hence, the performance and scalability are boosted.

  • Resource Management:
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Data structures such as linked lists boost the performance of functions like file directory management, processing scheduling queues, and memory allocation. All these functions build the core of resources and services management in larger corporations.

Conclusion

Data structures and algorithms help you efficiently build various computer programs. They follow a precise set of instructions in a specific order to provide the desired output. Your interest in data structure and algorithms can kickstart your data science career, and to initiate it, nothing is better than UpGrad’s Master of Science in Data Science program. This 2-year full-time program covers the cutting-edge curriculum derived from one of the Top 100 Best Global Universities in the World, the University of Arizona.

Sign up to learn more!

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Pavan Vadapalli

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Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1What are homogeneous and non-homogeneous data structures?

Homogeneous data structures include the matching data element type similar to the element collections you find in an array. But in non-homogeneous structures, data may not be of the matching type.

2How to learn data structures and algorithms?

(i) Firstly, learn HTML/CSS and then gradually move ahead to learn a programming language. (ii) Understand the computational complexity. (iii) Understand various data structures and algorithm types. (iv)Practice the use of data structures and algorithms. (v) Avail the on-the-job training. Try to get a job in software engineering to learn data structures and algorithms further while working on the job.

3What is the practical example of using data structures and algorithms?

Suppose you want to search for a word in the dictionary. Instead of flipping each page, you will open some pages, and if the word match is not found, you open the previous or next pages depending on the order of words to the current page. This practical example can be mapped to computer programming. It is a good example of selecting the right algorithm to solve a particular problem in less time.

4What is the Stack Data Structure, and where is it used?

Stack refers to an ordered list allowing insertion and deletion only from the top. It is a recursive Data Structure with a pointer to its top elements that informs us about the uppermost element of the stack. Stack is also called the LIFO method because the last element added into the stack will be available at the top and the first one to be popped out. Certain uses of the Stack Data Structure: 1) Memory Management 2) Expression evaluation 3) Backtracking 4) Function return and calling

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