- Blog Categories
- Software Development
- Data Science
- AI/ML
- Marketing
- General
- MBA
- Management
- Legal
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- Software Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Explore Skills
- Management Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
What are Data Structures & Algorithm
Updated on 05 June, 2023
5.45K+ views
• 8 min read
Table of Contents
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:
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:
- Array Data Structure
- Stack Data Structure
- Queue Data Structure
- 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.
Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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:
- Graph Data Structure
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
Check our US - Data Science Programs
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:
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!
Frequently Asked Questions (FAQs)
1. What 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.
2. How 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.
3. What 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.
4. What 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