WhatsApp is no longer a platform just for chat.
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I have curated the list of best WhatsApp channels to learn coding & data science for FREE
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It's an educational goldmine.
If you do, youβre 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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Coding Interviews
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Guys, Big Announcement!
Weβve officially hit 2 MILLION followers β and itβs time to take our Python journey to the next level!
Iβm super excited to launch the 30-Day Python Coding Challenge β perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python β bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereβs what youβll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- 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 β 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 β Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles 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 script)
- Final Project (Choose, build, and polish your app!)
React with β€οΈ 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
Weβve officially hit 2 MILLION followers β and itβs time to take our Python journey to the next level!
Iβm super excited to launch the 30-Day Python Coding Challenge β perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.
This challenge is your daily dose of Python β bite-sized lessons with hands-on projects so you actually code every day and level up fast.
Hereβs what youβll learn over the next 30 days:
Week 1: Python Fundamentals
- Variables & Data Types (Build your own bio/profile script)
- 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 β 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 β Build Cool Apps
- JSON & APIs (Fetch weather data)
- Web Scraping (Extract titles 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 script)
- Final Project (Choose, build, and polish your app!)
React with β€οΈ 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
β€1
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://t.me/datasciencefree
ENJOY LEARNING ππ
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://t.me/datasciencefree
ENJOY LEARNING ππ
β€2
Git Commands
π git init β Initialize a new Git repository
π₯ git clone <repo> β Clone a repository
π git status β Check the status of your repository
β git add <file> β Add a file to the staging area
π git commit -m "message" β Commit changes with a message
π git push β Push changes to a remote repository
β¬οΈ git pull β Fetch and merge changes from a remote repository
Branching
π git branch β List all branches
π± git branch <name> β Create a new branch
π git checkout <branch> β Switch to a branch
π git merge <branch> β Merge a branch into the current branch
β‘οΈ git rebase <branch> β Apply commits on top of another branch
Undo & Fix Mistakes
βͺ git reset --soft HEAD~1 β Undo the last commit but keep changes
β git reset --hard HEAD~1 β Undo the last commit and discard changes
π git revert <commit> β Create a new commit that undoes a specific commit
Logs & History
π git log β Show commit history
π git log --oneline --graph --all β View commit history in a simple graph
Stashing
π₯ git stash β Save changes without committing
π git stash pop β Apply stashed changes and remove them from stash
Remote & Collaboration
π git remote -v β View remote repositories
π‘ git fetch β Fetch changes without merging
π΅οΈ git diff β Compare changes
Donβt forget to react β€οΈ if youβd like to see more content like this!
π git init β Initialize a new Git repository
π₯ git clone <repo> β Clone a repository
π git status β Check the status of your repository
β git add <file> β Add a file to the staging area
π git commit -m "message" β Commit changes with a message
π git push β Push changes to a remote repository
β¬οΈ git pull β Fetch and merge changes from a remote repository
Branching
π git branch β List all branches
π± git branch <name> β Create a new branch
π git checkout <branch> β Switch to a branch
π git merge <branch> β Merge a branch into the current branch
β‘οΈ git rebase <branch> β Apply commits on top of another branch
Undo & Fix Mistakes
βͺ git reset --soft HEAD~1 β Undo the last commit but keep changes
β git reset --hard HEAD~1 β Undo the last commit and discard changes
π git revert <commit> β Create a new commit that undoes a specific commit
Logs & History
π git log β Show commit history
π git log --oneline --graph --all β View commit history in a simple graph
Stashing
π₯ git stash β Save changes without committing
π git stash pop β Apply stashed changes and remove them from stash
Remote & Collaboration
π git remote -v β View remote repositories
π‘ git fetch β Fetch changes without merging
π΅οΈ git diff β Compare changes
Donβt forget to react β€οΈ if youβd like to see more content like this!
β€5
π2π₯1
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 β 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#
Microsoftβs 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 itβs essential: Every application needs a database β 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 itβs rising: Simple, fast, and built for scale β 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 β 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#
Microsoftβs 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 itβs essential: Every application needs a database β 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 itβs rising: Simple, fast, and built for scale β 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
β€1
1. What is the difference between SQL and MySQL?
SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle or IBM DB2, that is used to manage SQL databases.
2. What is a Cross-Join?
Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables. If a WHERE clause is used in cross join then the query will work like an INNER JOIN.
3. What is a Stored Procedure?
A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.
4. What is Pattern Matching in SQL?
SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.
SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle or IBM DB2, that is used to manage SQL databases.
2. What is a Cross-Join?
Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables. If a WHERE clause is used in cross join then the query will work like an INNER JOIN.
3. What is a Stored Procedure?
A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.
4. What is Pattern Matching in SQL?
SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.
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