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!

🚀 Roadmap to Become a Software Architect 👨‍💻

📂 Programming & Development Fundamentals
 ∟📂 Master One or More Programming Languages (Java, C#, Python, etc.)
  ∟📂 Learn Data Structures & Algorithms
   ∟📂 Understand Design Patterns & Best Practices

📂 Software Design & Architecture Principles
 ∟📂 Learn SOLID Principles & Clean Code Practices
  ∟📂 Master Object-Oriented & Functional Design
   ∟📂 Understand Domain-Driven Design (DDD)

📂 System Design & Scalability
 ∟📂 Learn Microservices & Monolithic Architectures
  ∟📂 Understand Load Balancing, Caching & CDNs
   ∟📂 Dive into CAP Theorem & Event-Driven Architecture

📂 Databases & Storage Solutions
 ∟📂 Master SQL & NoSQL Databases
  ∟📂 Learn Database Scaling & Sharding Strategies
   ∟📂 Understand Data Warehousing & ETL Processes

📂 Cloud Computing & DevOps
 ∟📂 Learn Cloud Platforms (AWS, Azure, GCP)
  ∟📂 Understand CI/CD & Infrastructure as Code (IaC)
   ∟📂 Work with Containers & Kubernetes

📂 Security & Performance Optimization
 ∟📂 Master Secure Coding Practices
  ∟📂 Learn Authentication & Authorization (OAuth, JWT)
   ∟📂 Optimize System Performance & Reliability

📂 Project Management & Communication
 ∟📂 Work with Agile & Scrum Methodologies
  ∟📂 Collaborate with Cross-Functional Teams
   ∟📂 Improve Technical Documentation & Decision-Making

📂 Real-World Experience & Leadership
 ∟📂 Design & Build Scalable Software Systems
  ∟📂 Contribute to Open-Source & Architectural Discussions
   ∟📂 Mentor Developers & Lead Engineering Teams

📂 Interview Preparation & Career Growth
 ∟📂 Solve System Design Challenges
  ∟📂 Master Architectural Case Studies
   ∟📂 Network & Apply for Software Architect Roles

✅ Get Hired as a Software Architect

React "❤️" for More 👨‍💻

𝗛𝗼𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗮𝘀𝘁 (𝗘𝘃𝗲𝗻 𝗜𝗳 𝗬𝗼𝘂'𝘃𝗲 𝗡𝗲𝘃𝗲𝗿 𝗖𝗼𝗱𝗲𝗱 𝗕𝗲𝗳𝗼𝗿𝗲!)🐍🚀

Python is everywhere—web dev, data science, automation, AI…
But where should YOU start if you're a beginner?

Don’t worry. Here’s a 6-step roadmap to master Python the smart way (no fluff, just action)👇

🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics (Don’t Skip This!)
✅ Variables, data types (int, float, string, bool)
✅ Loops (for, while), conditionals (if/else)
✅ Functions and user input
Start with:
Python.org Docs
YouTube: Programming with Mosh / CodeWithHarry
Platforms: W3Schools / SoloLearn / FreeCodeCamp
Spend a week here.

Practice > Theory.

🔹 𝗦𝘁𝗲𝗽 𝟮: Automate Boring Stuff (It’s Fun + Useful!)
✅ Rename files in bulk
✅ Auto-fill forms
✅ Web scraping with BeautifulSoup or Selenium
Read: “Automate the Boring Stuff with Python”
It’s beginner-friendly and practical!

🔹 𝗦𝘁𝗲𝗽 𝟯: Build Mini Projects (Your Confidence Booster)
✅ Calculator app
✅ Dice roll simulator
✅ Password generator
✅ Number guessing game

These small projects teach logic, problem-solving, and syntax in action.

🔹 𝗦𝘁𝗲𝗽 𝟰: Dive Into Libraries (Python’s Superpower)
✅ Pandas and NumPy – for data
✅ Matplotlib – for visualizations
✅ Requests – for APIs
✅ Tkinter – for GUI apps
✅ Flask – for web apps

Libraries are what make Python powerful. Learn one at a time with a mini project.

🔹 𝗦𝘁𝗲𝗽 𝟱: Use Git + GitHub (Be a Real Dev)
✅ Track your code with Git
✅ Upload projects to GitHub
✅ Write clear README files
✅ Contribute to open source repos

Your GitHub profile = Your online CV. Keep it active!

🔹 𝗦𝘁𝗲𝗽 𝟲: Build a Capstone Project (Level-Up!)
✅ A weather dashboard (API + Flask)
✅ A personal expense tracker
✅ A web scraper that sends email alerts
✅ A basic portfolio website in Python + Flask

Pick something that solves a real problem—bonus if it helps you in daily life!

🎯 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝘆𝘁𝗵𝗼𝗻 = 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴

You don’t need to memorize code. Understand the logic.
Google is your best friend. Practice is your real teacher.

Python Resources: https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a

ENJOY LEARNING 👍👍

Best GitHub Repos for Computer Science Students💥!!!!


💠 Awesome:
https://github.com/sindresorhus/awesome

💠 Developer Roadmap:

https://github.com/kamranahmedse/developer-roadmap

💠 The Algorithms:

https://github.com/TheAlgorithms

💠 Tech Interview Handbook:

https://github.com/yangshun/tech-interview-handbook

💠 Project Based Learning:

https://github.com/practical-tutorials/project-based-learning

💠 Awesome Cheatsheets:

https://github.com/LeCoupa/awesome-cheatsheets

💠 Front End Checklist:
https://github.com/thedaviddias/Front-End-Checklist

Credits: https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43

Sample email template to reach out to HR’s as fresher

Hi Jasneet,

I recently came across your LinkedIn post seeking a React.js developer intern, and I am writing to express my interest in the position at Airtel. As a recent graduate, I am eager to begin my career and am excited about the opportunity.

I am a quick learner and have developed a strong set of dynamic and user-friendly web applications using various technologies, including HTML, CSS, JavaScript, Bootstrap, React.js, Vue.js, PHP, and MySQL. I am also well-versed in creating reusable components, implementing responsive designs, and ensuring cross-browser compatibility.

I am confident that my eagerness to learn and strong work ethic will make me an asset to your team.

I have attached my resume for your review. Thank you for considering my application. I look forward to hearing from you soon.

Thanks!

I hope you will found this helpful 🙂

If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ Master Advanced SQL

Foundations: Learn database structures, tables, and relationships.

Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.

Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.

JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.

Advanced Concepts: CTEs, window functions, and query optimization.

Metric Development: Build and report metrics effectively.


2️⃣ Study Statistics & A/B Testing

Denoscriptive Statistics: Know your mean, median, mode, and standard deviation.

Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.

Probability: Understand basic probability and Bayes' theorem.

Intro to ML: Start with linear regression, decision trees, and K-means clustering.

Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.

A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ Learn Python for Data

Data Manipulation: Use pandas for data cleaning and manipulation.

Data Visualization: Explore matplotlib and seaborn for creating visualizations.

Hypothesis Testing: Dive into scipy for statistical testing.

Basic Modeling: Practice building models with scikit-learn.


4️⃣ Develop Product Sense

Product Management Basics: Manage projects and understand the product life cycle.

Data-Driven Strategy: Leverage data to inform decisions and measure success.

Metrics in Business: Define and evaluate metrics that matter to the business.


5️⃣ Hone Soft Skills

Communication: Clearly explain data findings to technical and non-technical audiences.

Collaboration: Work effectively in teams.

Time Management: Prioritize and manage projects efficiently.

Self-Reflection: Regularly assess and improve your skills.


6️⃣ Bonus: Basic Data Engineering

Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.

ETL: Set up extraction jobs, manage dependencies, clean and validate data.

Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.

I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Like if you need similar content 😄👍