WhatsApp is no longer a platform just for chat.
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It's an educational goldmine.
If you do, you’re sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
Free Courses with Certificate
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Guys, Big Announcement!
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile noscript)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract noscripts from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer noscript)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!
I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Here’s what you’ll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile noscript)
- Operators (Mini calculator to sharpen math skills)
- Strings & String Methods (Word counter & palindrome checker)
- Lists & Tuples (Manage a grocery list like a pro)
- Dictionaries & Sets (Create your own contact book)
- Conditionals (Make a guess-the-number game)
- Loops (Multiplication tables & pattern printing)
Week 2: Functions & Logic — Make Your Code Smarter
- Functions (Prime number checker)
- Function Arguments (Tip calculator with custom tips)
- Recursion Basics (Factorials & Fibonacci series)
- Lambda, map & filter (Process lists efficiently)
- List Comprehensions (Filter odd/even numbers easily)
- Error Handling (Build a safe input reader)
- Review + Mini Project (Command-line to-do list)
Week 3: Files, Modules & OOP
- Reading & Writing Files (Save and load notes)
- Custom Modules (Create your own utility math module)
- Classes & Objects (Student grade tracker)
- Inheritance & OOP (RPG character system)
- Dunder Methods (Build a custom string class)
- OOP Mini Project (Simple bank account system)
- Review & Practice (Quiz app using OOP concepts)
Week 4: Real-World Python & APIs — Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract noscripts from HTML)
- Regular Expressions (Find emails & phone numbers)
- Tkinter GUI (Create a simple counter app)
- CLI Tools (Command-line calculator with argparse)
- Automation (File organizer noscript)
- Final Project (Choose, build, and polish your app!)
React with ❤️ if you're ready for this new journey
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661
❤3
Top 10 essential data science terminologies
1. Machine Learning: A subset of artificial intelligence that involves building algorithms that can learn from and make predictions or decisions based on data.
2. Big Data: Extremely large datasets that require specialized tools and techniques to analyze and extract insights from.
3. Data Mining: The process of discovering patterns, trends, and insights in large datasets using various methods such as machine learning and statistical analysis.
4. Predictive Analytics: The use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data.
5. Natural Language Processing (NLP): The field of study that focuses on enabling computers to understand, interpret, and generate human language.
6. Neural Networks: A type of machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes that can learn from data.
7. Feature Engineering: The process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models.
8. Data Visualization: The graphical representation of data to help users understand and interpret complex datasets more easily.
9. Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data.
10. Ensemble Learning: A technique that combines multiple machine learning models to improve predictive performance and reduce overfitting.
Credits: https://news.1rj.ru/str/datasciencefree
ENJOY LEARNING 👍👍
1. Machine Learning: A subset of artificial intelligence that involves building algorithms that can learn from and make predictions or decisions based on data.
2. Big Data: Extremely large datasets that require specialized tools and techniques to analyze and extract insights from.
3. Data Mining: The process of discovering patterns, trends, and insights in large datasets using various methods such as machine learning and statistical analysis.
4. Predictive Analytics: The use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data.
5. Natural Language Processing (NLP): The field of study that focuses on enabling computers to understand, interpret, and generate human language.
6. Neural Networks: A type of machine learning model inspired by the structure and function of the human brain, consisting of interconnected nodes that can learn from data.
7. Feature Engineering: The process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models.
8. Data Visualization: The graphical representation of data to help users understand and interpret complex datasets more easily.
9. Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data.
10. Ensemble Learning: A technique that combines multiple machine learning models to improve predictive performance and reduce overfitting.
Credits: https://news.1rj.ru/str/datasciencefree
ENJOY LEARNING 👍👍
❤2
Git Commands
🛠 git init – Initialize a new Git repository
📥 git clone <repo> – Clone a repository
📊 git status – Check the status of your repository
➕ git add <file> – Add a file to the staging area
📝 git commit -m "message" – Commit changes with a message
🚀 git push – Push changes to a remote repository
⬇️ git pull – Fetch and merge changes from a remote repository
Branching
📌 git branch – List all branches
🌱 git branch <name> – Create a new branch
🔄 git checkout <branch> – Switch to a branch
🔗 git merge <branch> – Merge a branch into the current branch
⚡️ git rebase <branch> – Apply commits on top of another branch
Undo & Fix Mistakes
⏪ git reset --soft HEAD~1 – Undo the last commit but keep changes
❌ git reset --hard HEAD~1 – Undo the last commit and discard changes
🔄 git revert <commit> – Create a new commit that undoes a specific commit
Logs & History
📖 git log – Show commit history
🌐 git log --oneline --graph --all – View commit history in a simple graph
Stashing
📥 git stash – Save changes without committing
🎭 git stash pop – Apply stashed changes and remove them from stash
Remote & Collaboration
🌍 git remote -v – View remote repositories
📡 git fetch – Fetch changes without merging
🕵️ git diff – Compare changes
Don’t forget to react ❤️ if you’d like to see more content like this!
🛠 git init – Initialize a new Git repository
📥 git clone <repo> – Clone a repository
📊 git status – Check the status of your repository
➕ git add <file> – Add a file to the staging area
📝 git commit -m "message" – Commit changes with a message
🚀 git push – Push changes to a remote repository
⬇️ git pull – Fetch and merge changes from a remote repository
Branching
📌 git branch – List all branches
🌱 git branch <name> – Create a new branch
🔄 git checkout <branch> – Switch to a branch
🔗 git merge <branch> – Merge a branch into the current branch
⚡️ git rebase <branch> – Apply commits on top of another branch
Undo & Fix Mistakes
⏪ git reset --soft HEAD~1 – Undo the last commit but keep changes
❌ git reset --hard HEAD~1 – Undo the last commit and discard changes
🔄 git revert <commit> – Create a new commit that undoes a specific commit
Logs & History
📖 git log – Show commit history
🌐 git log --oneline --graph --all – View commit history in a simple graph
Stashing
📥 git stash – Save changes without committing
🎭 git stash pop – Apply stashed changes and remove them from stash
Remote & Collaboration
🌍 git remote -v – View remote repositories
📡 git fetch – Fetch changes without merging
🕵️ git diff – Compare changes
Don’t forget to react ❤️ if you’d like to see more content like this!
❤5
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7 Most Popular Programming Languages in 2025
1. Python
The Jack of All Trades
Why it's loved: Simple syntax, huge community, beginner-friendly.
Used for: Data Science, Machine Learning, Web Development, Automation.
Who uses it: Data analysts, backend developers, researchers, even kids learning to code.
2. JavaScript
The Language of the Web
Why it's everywhere: Runs in every browser, now also on servers (Node.js).
Used for: Frontend & backend web apps, interactive UI, full-stack apps.
Who uses it: Web developers, app developers, UI/UX enthusiasts.
3. Java
The Enterprise Backbone
Why it stands strong: Portable, secure, scalable — runs on everything from desktops to Android devices.
Used for: Android apps, enterprise software, backend systems.
Who uses it: Large corporations, Android developers, system architects.
4. C/C++
The Power Players
Why they matter: Super fast, close to the hardware, great for performance-critical apps.
Used for: Game engines, operating systems, embedded systems.
Who uses it: System programmers, game developers, performance-focused engineers.
5. C#
Microsoft’s Darling
Why it's growing: Built into the .NET ecosystem, great for Windows apps and games.
Used for: Desktop applications, Unity game development, enterprise tools.
Who uses it: Game developers, enterprise app developers, Windows lovers.
6. SQL
The Language of Data
Why it’s essential: Every application needs a database — SQL helps you talk to it.
Used for: Querying databases, reporting, analytics.
Who uses it: Data analysts, backend devs, business intelligence professionals.
7. Go (Golang)
The Modern Minimalist
Why it’s rising: Simple, fast, and built for scale — ideal for cloud-native apps.
Used for: Web servers, microservices, distributed systems.
Who uses it: Backend engineers, DevOps, cloud developers.
Free Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
1. Python
The Jack of All Trades
Why it's loved: Simple syntax, huge community, beginner-friendly.
Used for: Data Science, Machine Learning, Web Development, Automation.
Who uses it: Data analysts, backend developers, researchers, even kids learning to code.
2. JavaScript
The Language of the Web
Why it's everywhere: Runs in every browser, now also on servers (Node.js).
Used for: Frontend & backend web apps, interactive UI, full-stack apps.
Who uses it: Web developers, app developers, UI/UX enthusiasts.
3. Java
The Enterprise Backbone
Why it stands strong: Portable, secure, scalable — runs on everything from desktops to Android devices.
Used for: Android apps, enterprise software, backend systems.
Who uses it: Large corporations, Android developers, system architects.
4. C/C++
The Power Players
Why they matter: Super fast, close to the hardware, great for performance-critical apps.
Used for: Game engines, operating systems, embedded systems.
Who uses it: System programmers, game developers, performance-focused engineers.
5. C#
Microsoft’s Darling
Why it's growing: Built into the .NET ecosystem, great for Windows apps and games.
Used for: Desktop applications, Unity game development, enterprise tools.
Who uses it: Game developers, enterprise app developers, Windows lovers.
6. SQL
The Language of Data
Why it’s essential: Every application needs a database — SQL helps you talk to it.
Used for: Querying databases, reporting, analytics.
Who uses it: Data analysts, backend devs, business intelligence professionals.
7. Go (Golang)
The Modern Minimalist
Why it’s rising: Simple, fast, and built for scale — ideal for cloud-native apps.
Used for: Web servers, microservices, distributed systems.
Who uses it: Backend engineers, DevOps, cloud developers.
Free Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
❤1