DSA Handwritten Notes
❤5👍2
Important skills every self-taught developer should master:
💻 HTML, CSS & JavaScript — the foundation of web development
⚙️ Git & GitHub — track changes and collaborate effectively
🧠 Problem-solving — break down and debug complex issues
🗄️ Basic SQL — manage and query data efficiently
🧩 APIs — fetch and use data from external sources
🧱 Frameworks — like React, Flask, or Django to build faster
🧼 Clean Code — write readable, maintainable code
📦 Package Managers — like npm or pip for managing libraries
🚀 Deployment — host your projects for the world to see
Web Development Resources: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
💻 HTML, CSS & JavaScript — the foundation of web development
⚙️ Git & GitHub — track changes and collaborate effectively
🧠 Problem-solving — break down and debug complex issues
🗄️ Basic SQL — manage and query data efficiently
🧩 APIs — fetch and use data from external sources
🧱 Frameworks — like React, Flask, or Django to build faster
🧼 Clean Code — write readable, maintainable code
📦 Package Managers — like npm or pip for managing libraries
🚀 Deployment — host your projects for the world to see
Web Development Resources: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
👍1
Don't overwhelm to learn JavaScript, JavaScript is only this much
1.Variables
• var
• let
• const
2. Data Types
• number
• string
• boolean
• null
• undefined
• symbol
3.Declaring variables
• var
• let
• const
4.Expressions
Primary expressions
• this
• Literals
• []
• {}
• function
• class
• function*
• async function
• async function*
• /ab+c/i
• string
• ( )
Left-hand-side expressions
• Property accessors
• ?.
• new
• new .target
• import.iss.oneta
• super
• import()
5.operators
• Arithmetic Operators: +, -, *, /, %
• Comparison Operators: ==, ===, !=, !==, <, >, <=, >=
• Logical Operators: &&, ||, !
6.Control Structures
• if
• else if
• else
• switch
• case
• default
7.Iterations/Loop
• do...while
• for
• for...in
• for...of
• for await...of
• while
8.Functions
• Arrow Functions
• Default parameters
• Rest parameters
• arguments
• Method definitions
• getter
• setter
9.Objects and Arrays
• Object Literal: { key: value }
• Array Literal: [element1, element2, ...]
• Object Methods and Properties
• Array Methods: push(), pop(), shift(), unshift(),
splice(), slice(), forEach(), map(), filter()
10.Classes and Prototypes
• Class Declaration
• Constructor Functions
• Prototypal Inheritance
• extends keyword
• super keyword
• Private class features
• Public class fields
• static
• Static initialization blocks
11.Error Handling
• try,
• catch,
• finally (exception handling)
ADVANCED CONCEPTS
12.Closures
• Lexical Scope
• Function Scope
• Closure Use Cases
13.Asynchronous JavaScript
• Callback Functions
• Promises
• async/await Syntax
• Fetch API
• XMLHttpRequest
14.Modules
• import and export Statements (ES6 Modules)
• CommonJS Modules (require, module.exports)
15.Event Handling
• Event Listeners
• Event Object
• Bubbling and Capturing
16.DOM Manipulation
• Selecting DOM Elements
• Modifying Element Properties
• Creating and Appending Elements
17.Regular Expressions
• Pattern Matching
• RegExp Methods: test(), exec(), match(), replace()
18.Browser APIs
• localStorage and sessionStorage
• navigator Object
• Geolocation API
• Canvas API
19.Web APIs
• setTimeout(), setInterval()
• XMLHttpRequest
• Fetch API
• WebSockets
20.Functional Programming
• Higher-Order Functions
• map(), reduce(), filter()
• Pure Functions and Immutability
21.Promises and Asynchronous Patterns
• Promise Chaining
• Error Handling with Promises
• Async/Await
22.ES6+ Features
• Template Literals
• Destructuring Assignment
• Rest and Spread Operators
• Arrow Functions
• Classes and Inheritance
• Default Parameters
• let, const Block Scoping
23.Browser Object Model (BOM)
• window Object
• history Object
• location Object
• navigator Object
24.Node.js Specific Concepts
• require()
• Node.js Modules (module.exports)
• File System Module (fs)
• npm (Node Package Manager)
25.Testing Frameworks
• Jasmine
• Mocha
• Jest
1.Variables
• var
• let
• const
2. Data Types
• number
• string
• boolean
• null
• undefined
• symbol
3.Declaring variables
• var
• let
• const
4.Expressions
Primary expressions
• this
• Literals
• []
• {}
• function
• class
• function*
• async function
• async function*
• /ab+c/i
• string
• ( )
Left-hand-side expressions
• Property accessors
• ?.
• new
• new .target
• import.iss.oneta
• super
• import()
5.operators
• Arithmetic Operators: +, -, *, /, %
• Comparison Operators: ==, ===, !=, !==, <, >, <=, >=
• Logical Operators: &&, ||, !
6.Control Structures
• if
• else if
• else
• switch
• case
• default
7.Iterations/Loop
• do...while
• for
• for...in
• for...of
• for await...of
• while
8.Functions
• Arrow Functions
• Default parameters
• Rest parameters
• arguments
• Method definitions
• getter
• setter
9.Objects and Arrays
• Object Literal: { key: value }
• Array Literal: [element1, element2, ...]
• Object Methods and Properties
• Array Methods: push(), pop(), shift(), unshift(),
splice(), slice(), forEach(), map(), filter()
10.Classes and Prototypes
• Class Declaration
• Constructor Functions
• Prototypal Inheritance
• extends keyword
• super keyword
• Private class features
• Public class fields
• static
• Static initialization blocks
11.Error Handling
• try,
• catch,
• finally (exception handling)
ADVANCED CONCEPTS
12.Closures
• Lexical Scope
• Function Scope
• Closure Use Cases
13.Asynchronous JavaScript
• Callback Functions
• Promises
• async/await Syntax
• Fetch API
• XMLHttpRequest
14.Modules
• import and export Statements (ES6 Modules)
• CommonJS Modules (require, module.exports)
15.Event Handling
• Event Listeners
• Event Object
• Bubbling and Capturing
16.DOM Manipulation
• Selecting DOM Elements
• Modifying Element Properties
• Creating and Appending Elements
17.Regular Expressions
• Pattern Matching
• RegExp Methods: test(), exec(), match(), replace()
18.Browser APIs
• localStorage and sessionStorage
• navigator Object
• Geolocation API
• Canvas API
19.Web APIs
• setTimeout(), setInterval()
• XMLHttpRequest
• Fetch API
• WebSockets
20.Functional Programming
• Higher-Order Functions
• map(), reduce(), filter()
• Pure Functions and Immutability
21.Promises and Asynchronous Patterns
• Promise Chaining
• Error Handling with Promises
• Async/Await
22.ES6+ Features
• Template Literals
• Destructuring Assignment
• Rest and Spread Operators
• Arrow Functions
• Classes and Inheritance
• Default Parameters
• let, const Block Scoping
23.Browser Object Model (BOM)
• window Object
• history Object
• location Object
• navigator Object
24.Node.js Specific Concepts
• require()
• Node.js Modules (module.exports)
• File System Module (fs)
• npm (Node Package Manager)
25.Testing Frameworks
• Jasmine
• Mocha
• Jest
👍4
Master Javascript :
The JavaScript Tree 👇
|
|── Variables
| ├── var
| ├── let
| └── const
|
|── Data Types
| ├── String
| ├── Number
| ├── Boolean
| ├── Object
| ├── Array
| ├── Null
| └── Undefined
|
|── Operators
| ├── Arithmetic
| ├── Assignment
| ├── Comparison
| ├── Logical
| ├── Unary
| └── Ternary (Conditional)
||── Control Flow
| ├── if statement
| ├── else statement
| ├── else if statement
| ├── switch statement
| ├── for loop
| ├── while loop
| └── do-while loop
|
|── Functions
| ├── Function declaration
| ├── Function expression
| ├── Arrow function
| └── IIFE (Immediately Invoked Function Expression)
|
|── Scope
| ├── Global scope
| ├── Local scope
| ├── Block scope
| └── Lexical scope
||── Arrays
| ├── Array methods
| | ├── push()
| | ├── pop()
| | ├── shift()
| | ├── unshift()
| | ├── splice()
| | ├── slice()
| | └── concat()
| └── Array iteration
| ├── forEach()
| ├── map()
| ├── filter()
| └── reduce()|
|── Objects
| ├── Object properties
| | ├── Dot notation
| | └── Bracket notation
| ├── Object methods
| | ├── Object.keys()
| | ├── Object.values()
| | └── Object.entries()
| └── Object destructuring
||── Promises
| ├── Promise states
| | ├── Pending
| | ├── Fulfilled
| | └── Rejected
| ├── Promise methods
| | ├── then()
| | ├── catch()
| | └── finally()
| └── Promise.all()
|
|── Asynchronous JavaScript
| ├── Callbacks
| ├── Promises
| └── Async/Await
|
|── Error Handling
| ├── try...catch statement
| └── throw statement
|
|── JSON (JavaScript Object Notation)
||── Modules
| ├── import
| └── export
|
|── DOM Manipulation
| ├── Selecting elements
| ├── Modifying elements
| └── Creating elements
|
|── Events
| ├── Event listeners
| ├── Event propagation
| └── Event delegation
|
|── AJAX (Asynchronous JavaScript and XML)
|
|── Fetch API
||── ES6+ Features
| ├── Template literals
| ├── Destructuring assignment
| ├── Spread/rest operator
| ├── Arrow functions
| ├── Classes
| ├── let and const
| ├── Default parameters
| ├── Modules
| └── Promises
|
|── Web APIs
| ├── Local Storage
| ├── Session Storage
| └── Web Storage API
|
|── Libraries and Frameworks
| ├── React
| ├── Angular
| └── Vue.js
||── Debugging
| ├── Console.log()
| ├── Breakpoints
| └── DevTools
|
|── Others
| ├── Closures
| ├── Callbacks
| ├── Prototypes
| ├── this keyword
| ├── Hoisting
| └── Strict mode
|
| END __
The JavaScript Tree 👇
|
|── Variables
| ├── var
| ├── let
| └── const
|
|── Data Types
| ├── String
| ├── Number
| ├── Boolean
| ├── Object
| ├── Array
| ├── Null
| └── Undefined
|
|── Operators
| ├── Arithmetic
| ├── Assignment
| ├── Comparison
| ├── Logical
| ├── Unary
| └── Ternary (Conditional)
||── Control Flow
| ├── if statement
| ├── else statement
| ├── else if statement
| ├── switch statement
| ├── for loop
| ├── while loop
| └── do-while loop
|
|── Functions
| ├── Function declaration
| ├── Function expression
| ├── Arrow function
| └── IIFE (Immediately Invoked Function Expression)
|
|── Scope
| ├── Global scope
| ├── Local scope
| ├── Block scope
| └── Lexical scope
||── Arrays
| ├── Array methods
| | ├── push()
| | ├── pop()
| | ├── shift()
| | ├── unshift()
| | ├── splice()
| | ├── slice()
| | └── concat()
| └── Array iteration
| ├── forEach()
| ├── map()
| ├── filter()
| └── reduce()|
|── Objects
| ├── Object properties
| | ├── Dot notation
| | └── Bracket notation
| ├── Object methods
| | ├── Object.keys()
| | ├── Object.values()
| | └── Object.entries()
| └── Object destructuring
||── Promises
| ├── Promise states
| | ├── Pending
| | ├── Fulfilled
| | └── Rejected
| ├── Promise methods
| | ├── then()
| | ├── catch()
| | └── finally()
| └── Promise.all()
|
|── Asynchronous JavaScript
| ├── Callbacks
| ├── Promises
| └── Async/Await
|
|── Error Handling
| ├── try...catch statement
| └── throw statement
|
|── JSON (JavaScript Object Notation)
||── Modules
| ├── import
| └── export
|
|── DOM Manipulation
| ├── Selecting elements
| ├── Modifying elements
| └── Creating elements
|
|── Events
| ├── Event listeners
| ├── Event propagation
| └── Event delegation
|
|── AJAX (Asynchronous JavaScript and XML)
|
|── Fetch API
||── ES6+ Features
| ├── Template literals
| ├── Destructuring assignment
| ├── Spread/rest operator
| ├── Arrow functions
| ├── Classes
| ├── let and const
| ├── Default parameters
| ├── Modules
| └── Promises
|
|── Web APIs
| ├── Local Storage
| ├── Session Storage
| └── Web Storage API
|
|── Libraries and Frameworks
| ├── React
| ├── Angular
| └── Vue.js
||── Debugging
| ├── Console.log()
| ├── Breakpoints
| └── DevTools
|
|── Others
| ├── Closures
| ├── Callbacks
| ├── Prototypes
| ├── this keyword
| ├── Hoisting
| └── Strict mode
|
| END __
👍4❤1
📌 Python Cheatsheet: Master the Foundations & Beyond
Start learning Python →
⬇️ Core Python Building Blocks
Basic Commands
→ print() – Display output
→ input() – Get user input
→ len() – Get length of a data structure
→ type() – Get variable type
→ range() – Generate a sequence
→ help() – Get documentation
Data Types
→ int, float, bool, str – Numbers & text
→ list, tuple, dict, set – Data collections
Control Structures
→ if / elif / else – Conditional logic
→ for, while – Loops
→ break, continue, pass – Loop control
⬇️ Advanced Concepts
Functions & Classes
→ def, return, lambda – Define functions
→ class, init, self – Object-oriented programming
Modules
→ import, from ... import – Reuse code
⬇️ Special Tools
Exception Handling
→ try, except, finally, raise – Handle errors
File Handling
→ open(), read(), write(), close() – Manage files
Decorators & Generators
→ @decorator, yield – Extend or pause functions
List Comprehension
→ [x for x in list if condition] – Create lists efficiently
Like for more ❤️
Start learning Python →
⬇️ Core Python Building Blocks
Basic Commands
→ print() – Display output
→ input() – Get user input
→ len() – Get length of a data structure
→ type() – Get variable type
→ range() – Generate a sequence
→ help() – Get documentation
Data Types
→ int, float, bool, str – Numbers & text
→ list, tuple, dict, set – Data collections
Control Structures
→ if / elif / else – Conditional logic
→ for, while – Loops
→ break, continue, pass – Loop control
⬇️ Advanced Concepts
Functions & Classes
→ def, return, lambda – Define functions
→ class, init, self – Object-oriented programming
Modules
→ import, from ... import – Reuse code
⬇️ Special Tools
Exception Handling
→ try, except, finally, raise – Handle errors
File Handling
→ open(), read(), write(), close() – Manage files
Decorators & Generators
→ @decorator, yield – Extend or pause functions
List Comprehension
→ [x for x in list if condition] – Create lists efficiently
Like for more ❤️
❤6👍3
Backend Development – Essential Concepts 🚀
1️⃣ Backend vs. Frontend
Frontend – Handles UI/UX (HTML, CSS, JavaScript, React, Vue).
Backend – Manages server, database, APIs, and business logic.
2️⃣ Backend Programming Languages
Python – Django, Flask, FastAPI.
JavaScript – Node.js, Express.js.
Java – Spring Boot.
PHP – Laravel.
Ruby – Ruby on Rails.
Go – Gin, Echo.
3️⃣ Databases
SQL Databases – MySQL, PostgreSQL, MS SQL, MariaDB.
NoSQL Databases – MongoDB, Firebase, Cassandra, DynamoDB.
ORM (Object-Relational Mapping) – SQLAlchemy (Python), Sequelize (Node.js).
4️⃣ APIs & Web Services
REST API – Uses HTTP methods (GET, POST, PUT, DELETE).
GraphQL – Flexible API querying.
WebSockets – Real-time communication.
gRPC – High-performance communication.
5️⃣ Authentication & Security
JWT (JSON Web Token) – Secure user authentication.
OAuth 2.0 – Third-party authentication (Google, Facebook).
Hashing & Encryption – Protecting user data (bcrypt, AES).
CORS & CSRF Protection – Prevent security vulnerabilities.
6️⃣ Server & Hosting
Cloud Providers – AWS, Google Cloud, Azure.
Serverless Computing – AWS Lambda, Firebase Functions.
Docker & Kubernetes – Containerization and orchestration.
7️⃣ Caching & Performance Optimization
Redis & Memcached – Fast data caching.
Load Balancing – Distribute traffic efficiently.
CDN (Content Delivery Network) – Faster content delivery.
8️⃣ DevOps & Deployment
CI/CD Pipelines – GitHub Actions, Jenkins, GitLab CI.
Monitoring & Logging – Prometheus, ELK Stack.
Version Control – Git, GitHub, GitLab.
Like it if you need a complete tutorial on all these topics! 👍❤️
Web Development Best Resources
ENJOY LEARNING 👍👍
1️⃣ Backend vs. Frontend
Frontend – Handles UI/UX (HTML, CSS, JavaScript, React, Vue).
Backend – Manages server, database, APIs, and business logic.
2️⃣ Backend Programming Languages
Python – Django, Flask, FastAPI.
JavaScript – Node.js, Express.js.
Java – Spring Boot.
PHP – Laravel.
Ruby – Ruby on Rails.
Go – Gin, Echo.
3️⃣ Databases
SQL Databases – MySQL, PostgreSQL, MS SQL, MariaDB.
NoSQL Databases – MongoDB, Firebase, Cassandra, DynamoDB.
ORM (Object-Relational Mapping) – SQLAlchemy (Python), Sequelize (Node.js).
4️⃣ APIs & Web Services
REST API – Uses HTTP methods (GET, POST, PUT, DELETE).
GraphQL – Flexible API querying.
WebSockets – Real-time communication.
gRPC – High-performance communication.
5️⃣ Authentication & Security
JWT (JSON Web Token) – Secure user authentication.
OAuth 2.0 – Third-party authentication (Google, Facebook).
Hashing & Encryption – Protecting user data (bcrypt, AES).
CORS & CSRF Protection – Prevent security vulnerabilities.
6️⃣ Server & Hosting
Cloud Providers – AWS, Google Cloud, Azure.
Serverless Computing – AWS Lambda, Firebase Functions.
Docker & Kubernetes – Containerization and orchestration.
7️⃣ Caching & Performance Optimization
Redis & Memcached – Fast data caching.
Load Balancing – Distribute traffic efficiently.
CDN (Content Delivery Network) – Faster content delivery.
8️⃣ DevOps & Deployment
CI/CD Pipelines – GitHub Actions, Jenkins, GitLab CI.
Monitoring & Logging – Prometheus, ELK Stack.
Version Control – Git, GitHub, GitLab.
Like it if you need a complete tutorial on all these topics! 👍❤️
Web Development Best Resources
ENJOY LEARNING 👍👍
👍2❤1
9 tips to learn Data Structures & Algorithms (DSA) effectively:
Master one language first (like Python, Java, or C++)
Start with basics: arrays, strings, linked lists
Practice consistently on coding platforms
Visualize problems to understand them better
Learn patterns — sliding window, two pointers, recursion
Understand time and space complexity
Solve problems before reading solutions
Revisit and revise tough problems
Build a strong foundation before diving into advanced topics
Coding Interview Resources:👇 https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING 👍👍
Master one language first (like Python, Java, or C++)
Start with basics: arrays, strings, linked lists
Practice consistently on coding platforms
Visualize problems to understand them better
Learn patterns — sliding window, two pointers, recursion
Understand time and space complexity
Solve problems before reading solutions
Revisit and revise tough problems
Build a strong foundation before diving into advanced topics
Coding Interview Resources:👇 https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING 👍👍
👍5❤1
🔗 Master 8 Essential Machine Learning Algorithms
❤5
🏟 Here is a complete roadmap to learn Data Structures and Algorithms (DSA) 🏟
1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.
2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.
3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.
4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.
5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.
6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.
7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.
8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.
9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.
10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
1. Basics of Programming: Start by learning the basics of a programming language like Python, Java, or C++. Understand concepts like variables, loops, functions, and arrays.
2. Data Structures: Study fundamental data structures like arrays, linked lists, stacks, queues, trees, graphs, and hash tables. Understand the operations that can be performed on these data structures and their time complexities.
3. Algorithms: Learn common algorithms like searching, sorting, recursion, dynamic programming, greedy algorithms, and divide and conquer. Understand how these algorithms work and their time complexities.
4. Problem Solving: Practice solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Start with easy problems and gradually move to medium and hard problems.
5. Complexity Analysis: Learn how to analyze the time and space complexity of algorithms. Understand Big O notation and how to calculate the complexity of different algorithms.
6. Advanced Data Structures: Study advanced data structures like AVL trees, B-trees, tries, segment trees, and fenwick trees. Understand when and how to use these data structures in problem-solving.
7. Graph Algorithms: Learn graph traversal algorithms like BFS and DFS. Study algorithms like Dijkstra's algorithm, Bellman-Ford algorithm, and Floyd-Warshall algorithm for shortest path problems.
8. Dynamic Programming: Master dynamic programming techniques for solving complex problems efficiently. Practice solving dynamic programming problems to build your skills.
9. Practice and Review: Regularly practice coding problems and review your solutions. Analyze your mistakes and learn from them to improve your problem-solving skills.
10. Mock Interviews: Prepare for technical interviews by participating in mock interviews and solving interview-style coding problems. Practice explaining your thought process and reasoning behind your solutions.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
❤4
DSA INTERVIEW QUESTIONS AND ANSWERS
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
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All the best 👍👍
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
Credits: https://t.iss.one/free4unow_backup
All the best 👍👍
👍2
Let's explore some of the best open source projects by language.
1⃣ Best Python Open Source Projects
🚣♂ TensorFlow
🚣♂ Matplotlib
🚣♂ Flask
🚣♂ Django
🚣♂ PyTorch
2⃣ Best JavaScript Open Source Projects
🚣♂ React
🚣♂ Node.JS
🚣♂ jQuery
3⃣ Best C++ Open Source Projects
🚣♂ Serenity
🚣♂ MongoDB
🚣♂ SonarSource
🚣♂ OBS Studio
🚣♂ Electron
4⃣ Best Java Open Source Projects
🚣♂ Mockito
🚣♂ Realm
🚣♂ Jenkins
🚣♂ Guava
🚣♂ Moshi
It's time to start developing your own open source projects. Explore the projects
1⃣ Best Python Open Source Projects
🚣♂ TensorFlow
🚣♂ Matplotlib
🚣♂ Flask
🚣♂ Django
🚣♂ PyTorch
2⃣ Best JavaScript Open Source Projects
🚣♂ React
🚣♂ Node.JS
🚣♂ jQuery
3⃣ Best C++ Open Source Projects
🚣♂ Serenity
🚣♂ MongoDB
🚣♂ SonarSource
🚣♂ OBS Studio
🚣♂ Electron
4⃣ Best Java Open Source Projects
🚣♂ Mockito
🚣♂ Realm
🚣♂ Jenkins
🚣♂ Guava
🚣♂ Moshi
It's time to start developing your own open source projects. Explore the projects
❤1
Basics of Programming 👆✅
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