Famous programming languages and their frameworks


1. Python:

Frameworks:
Django
Flask
Pyramid
Tornado

2. JavaScript:

Frameworks (Front-End):
React
Angular
Vue.js
Ember.js
Frameworks (Back-End):
Node.js (Runtime)
Express.js
Nest.js
Meteor

3. Java:

Frameworks:
Spring Framework
Hibernate
Apache Struts
Play Framework

4. Ruby:

Frameworks:
Ruby on Rails (Rails)
Sinatra
Hanami

5. PHP:

Frameworks:
Laravel
Symfony
CodeIgniter
Yii
Zend Framework

6. C#:

Frameworks:
.NET Framework
ASP.NET
ASP.NET Core

7. Go (Golang):

Frameworks:
Gin
Echo
Revel

8. Rust:

Frameworks:
Rocket
Actix
Warp

9. Swift:

Frameworks (iOS/macOS):
SwiftUI
UIKit
Cocoa Touch

10. Kotlin:
- Frameworks (Android):
- Android Jetpack
- Ktor

11. TypeScript:
- Frameworks (Front-End):
- Angular
- Vue.js (with TypeScript)
- React (with TypeScript)

12. Scala:
- Frameworks:
- Play Framework
- Akka

13. Perl:
- Frameworks:
- Dancer
- Catalyst

14. Lua:
- Frameworks:
- OpenResty (for web development)

15. Dart:
- Frameworks:
- Flutter (for mobile app development)

16. R:
- Frameworks (for data science and statistics):
- Shiny
- ggplot2

17. Julia:
- Frameworks (for scientific computing):
- Pluto.jl
- Genie.jl

18. MATLAB:
- Frameworks (for scientific and engineering applications):
- Simulink

19. COBOL:
- Frameworks:
- COBOL-IT

20. Erlang:
- Frameworks:
- Phoenix (for web applications)

21. Groovy:
- Frameworks:
- Grails (for web applications)

Here are 10 popular programming languages based on versatile, widely-used, and in-demand languages:

1. Python – Ideal for beginners and professionals; used in web development, data analysis, AI, and more.

2. Java – A classic language for building enterprise applications, Android apps, and large-scale systems.

3. C – The foundation for many other languages; great for understanding low-level programming concepts.

4. C++ – Popular for game development, competitive programming, and performance-critical applications.

5. C# – Widely used for Windows applications, game development (Unity), and enterprise software.

6. Go (Golang) – A modern language designed for performance and scalability, popular in cloud services.

7. Rust – Known for its safety and performance, ideal for system-level programming.

8. Kotlin – The preferred language for Android development with modern features.

9. Swift – Used for developing iOS and macOS applications with simplicity and power.

10. PHP – A staple for web development, powering many websites and applications

If you want to Excel at Web Development and build stunning websites, master these essential skills:

Frontend:
• HTML, CSS, JavaScript – Core web technologies
• Flexbox & Grid – Master modern CSS layouts
• Responsive Design – Make websites mobile-friendly
• JavaScript ES6+ – Arrow functions, Promises, Async/Await
• React, Vue, or Angular – Modern frontend frameworks
• APIs & Fetch/Axios – Connect frontend with backend
• State Management – Redux, Vuex, or Context API

Backend:
• Node.js & Express.js – Build powerful server-side applications
• Databases – MySQL, PostgreSQL, MongoDB (NoSQL)
• RESTful APIs & GraphQL – Handle data efficiently
• Authentication – JWT, OAuth, and session management
• WebSockets – Real-time applications

DevOps & Deployment:
• Version Control – Git & GitHub
• CI/CD Pipelines – Automate deployments
• Cloud Hosting – AWS, Firebase, Vercel, Netlify
• Docker & Kubernetes – Scalable applications

Like it if you need a complete tutorial on all these topics! 👍❤️

You could completely change your life by the end of this year.

You could take another job, start learning a new language or skill, cut out junk food and start working out. You can do anything!

You have 100% control over your life.

Sometimes it doesn't feel like it. Like your life is passing you by, but you could make huge changes today that could effect the rest of your life.

Make a plan and do it!

Evolution of Programming Languages 🖥️


🔰Programming Languages🔰

1. JAVA:
More than 85% android apps are created using JAVA. It is also used in big (big means big) websites. It is a portable programming language which makes it easy to use on multi platforms.

2. Java Script:
Its a browser/client side language. It makes the webpage more interactive. Like for example when you enter a comment on Facebook then the whole page doesn’t load., just that comment is added. This kind of functionalities are added into webpages with JavaScript. Javascript brought about a revolution in webapps.

3. Assembly Language:
The most low level programming language because its nothing more than machine code written in human readable form. Its hard to write and you need to have deep understanding of computers to use this because you are really talking with it. Its very fast in terms of execution.

4. C:
Its a low level language too that’s why its fast. It is used to program operating system, computer games and software which need to be fast. It is hard to write but gives you more control of your computer.

5. C++ :
Its C with more features and those features make it more complex.

6. Perl:
A language which was developed to create small scripts easily . Programming in Perl is easy and efficient but the programs are comparatively slower.

7. Python:
Perl was made better and named Python. Its easy, efficient and flexible. You can automate things with python in a go.

8. Ruby:
Its similar to Python but it became popular when they created a web application development framework named Rails which lets developers to write their web application conveniently.

9. HTML and CSS:
HTML and CSS are languages not programming languages because they are just used display things on a website. They do not do any actual processing. HTML is used to create the basic structure of the website and then CSS is used to make it look good.

10. PHP:
It is used to process things in a website. It is server-sided language as it doesn’t get executed in user browser, but on the server. It can be used to generate dynamic webpage content.

11. SQL:
This is not exactly a programming language. It is used to interact with databases.

➡️ This list could be long because there are too many programming language but I introduced you to the popular ones.

❓Which Language Should Be Your First Programming Language?

✅ Suggestions..

1. Getting Started
Learn HTML & CSS. They are easy and will give you a basic idea of how programming works. You will be able to create your own webpages. After HTML you can go with PHP and SQL, so will have a good grasp over web designing and then you can go with python, C or Java. I assure you that PHP, HTML and SQL will be definitely useful in your hacking journey.

2. Understanding Computer And Programming Better
C..The classic C! C is one of the most foundational languages. If you learn C, you will have a deep knowledge of Computers and you will have a greater understanding of programming too, that will make you a better programmer. You will spend most of your time compiling though (just trying to crack a joke).

3. Too Eager To Create Programs?
Python! Python is very easy to learn and you can create a program which does something instead of programming calculators. Well Python doesn’t start you from the basics but with if you know python, you will be able to understand other languages better. One benefit of python is that you don’t need to compile the script to run it, just write one and run it.

React ❤️ for more

9 full-stack project ideas to build your portfolio:

🛍️ Online Store — product listings, cart, checkout, and payment integration

🗓️ Event Booking App — users can browse, book, and manage events

📚 Learning Platform — courses, quizzes, progress tracking

🏥 Appointment Scheduler — book and manage appointments with calendar UI

✍️ Blogging System — post creation, comments, likes, and user roles

💼 Job Board — post and search jobs, apply with resumes

🏠 Real Estate Listings — search, filter, and view property details

💬 Chat App — real-time messaging with sockets or Firebase

📊 Admin Dashboard — charts, user data, and analytics in one place

Like this post if you want me to cover the skills needed to build such projects ❤️

Web Development Resources: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z

Like it if you need a complete tutorial on all these projects! 👍❤️

Complete Python Roadmap 🐍👇

1. Introduction to Python
- Definition
- Purpose
- Python Installation
- Interpreter vs Compiler

2. Basic Python Syntax
- Print Statement
- Variables and Data Types
- Input and Output
- Operators

3. Control Flow
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Break and Continue Statements

4. Data Structures
- Lists
- Tuples
- Sets
- Dictionaries

5. Functions
- Function Definition
- Parameters and Return Values
- Lambda Functions

6. File Handling
- Reading from and Writing to Files
- Handling Exceptions

7. Modules and Packages
- Importing Modules
- Creating Packages

8. Object-Oriented Programming (OOP)
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction

9. Error Handling
- Try, Except Blocks
- Custom Exceptions

10. Advanced Data Structures
- List Comprehensions
- Generators
- Collections Module

11. Decorators and Generators
- Function Decorators
- Generator Functions

12. Working with APIs
- Making HTTP Requests
- JSON Handling

13. Database Interaction with Python
- Connecting to Databases
- CRUD Operations

14. Web Development with Flask/Django
- Flask/Django Setup
- Routing and Templates

15. Asynchronous Programming
- Async/Await
- Asyncio Library

16. Testing in Python
- Unit Testing
- Testing Frameworks (e.g., pytest)

17. Pythonic Code
- PEP 8 Style Guide
- Code Readability

18. Version Control (Git)
- Basic Commands
- Collaborative Development

19. Data Science Libraries
- NumPy
- Pandas
- Matplotlib

20. Machine Learning Basics
- Scikit-Learn
- Model Training and Evaluation

21. Web Scraping
- BeautifulSoup
- Scrapy

22. RESTful API Development
- Flask/Django Rest Framework

23. CI/CD Basics
- Continuous Integration
- Continuous Deployment

24. Deployment
- Deploying Python Applications
- Hosting Platforms (e.g., Heroku)

25. Security Best Practices
- Input Validation
- Handling Sensitive Data

26. Code Documentation
- Docstrings
- Generating Documentation

27. Community and Collaboration
- Open Source Contributions
- Forums and Conferences

Resources to Learn Python:

1. Free Course
- https://www.freecodecamp.org/learn/data-analysis-with-python/

2. Projects
- t.iss.one/pythonfreebootcamp/177
- t.iss.one/pythonspecialist/90

3. Books & Notes
- https://t.iss.one/dsabooks/99
- https://t.iss.one/dsabooks/101

4. Python Interview Preparation
- https://t.iss.one/PythonInterviews
- t.iss.one/DataAnalystInterview/63

Join @free4unow_backup for more Python resources.

Like this post if you want more content like this 😄❤️

ENJOY LEARNING 👍👍

Theoretical Questions for Coding Interviews on Basic Data Structures

1. What is a Data Structure?
A data structure is a way of organizing and storing data so that it can be accessed and modified efficiently. Common data structures include arrays, linked lists, stacks, queues, and trees.

2. What is an Array?
An array is a collection of elements, each identified by an index. It has a fixed size and stores elements of the same type in contiguous memory locations.

3. What is a Linked List?
A linked list is a linear data structure where elements (nodes) are stored non-contiguously. Each node contains a value and a reference (or link) to the next node. Unlike arrays, linked lists can grow dynamically.

4. What is a Stack?
A stack is a linear data structure that follows the Last In, First Out (LIFO) principle. The most recently added element is the first one to be removed. Common operations include push (add an element) and pop (remove an element).

5. What is a Queue?
A queue is a linear data structure that follows the First In, First Out (FIFO) principle. The first element added is the first one to be removed. Common operations include enqueue (add an element) and dequeue (remove an element).

6. What is a Binary Tree?
A binary tree is a hierarchical data structure where each node has at most two children, usually referred to as the left and right child. It is used for efficient searching and sorting.

7. What is the difference between an array and a linked list?

Array: Fixed size, elements stored in contiguous memory.

Linked List: Dynamic size, elements stored non-contiguously, each node points to the next.


8. What is the time complexity for accessing an element in an array vs. a linked list?

Array: O(1) for direct access by index.

Linked List: O(n) for access, as you must traverse the list from the start to find an element.


9. What is the time complexity for inserting or deleting an element in an array vs. a linked list?

Array:

Insertion/Deletion at the end: O(1).

Insertion/Deletion at the beginning or middle: O(n) because elements must be shifted.


Linked List:

Insertion/Deletion at the beginning: O(1).

Insertion/Deletion in the middle or end: O(n), as you need to traverse the list.



10. What is a HashMap (or Dictionary)?
A HashMap is a data structure that stores key-value pairs. It allows efficient lookups, insertions, and deletions using a hash function to map keys to values. Average time complexity for these operations is O(1).

Coding interview: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X

Use cases of top programming languages

Essential Programming Languages to Learn Data Science 👇👇

1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).

2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.

3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.

4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.

5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.

6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.

7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.

Free Resources to master data analytics concepts 👇👇

Data Analysis with R

Intro to Data Science

Practical Python Programming

SQL for Data Analysis

Java Essential Concepts

Machine Learning with Python

Data Science Project Ideas

Learning SQL FREE Book

Join @free4unow_backup for more free resources.

ENJOY LEARNING👍👍