If I wanted to get my opportunity to interview at Google or Amazon for SDE roles in the next 6-8 months…
Here’s exactly how I’d approach it (I’ve taught this to 100s of students and followed it myself to land interviews at 3+ FAANGs):
► Step 1: Learn to Code (from scratch, even if you’re from non-CS background)
I helped my sister go from zero coding knowledge (she studied Biology and Electrical Engineering) to landing a job at Microsoft.
We started with:
- A simple programming language (C++, Java, Python — pick one)
- FreeCodeCamp on YouTube for beginner-friendly lectures
- Key rule: Don’t just watch. Code along with the video line by line.
Time required: 30–40 days to get good with loops, conditions, syntax.
► Step 2: Start with DSA before jumping to development
Why?
- 90% of tech interviews in top companies focus on Data Structures & Algorithms
- You’ll need time to master it, so start early.
Start with:
- Arrays → Linked List → Stacks → Queues
- You can follow the DSA videos on my channel.
- Practice while learning is a must.
► Step 3: Follow a smart topic order
Once you’re done with basics, follow this path:
1. Searching & Sorting
2. Recursion & Backtracking
3. Greedy
4. Sliding Window & Two Pointers
5. Trees & Graphs
6. Dynamic Programming
7. Tries, Heaps, and Union Find
Make revision notes as you go — note down how you solved each question, what tricks worked, and how you optimized it.
► Step 4: Start giving contests (don’t wait till you’re “ready”)
Most students wait to “finish DSA” before attempting contests.
That’s a huge mistake.
Contests teach you:
- Time management under pressure
- Handling edge cases
- Thinking fast
Platforms: LeetCode Weekly/ Biweekly, Codeforces, AtCoder, etc.
And after every contest, do upsolving — solve the questions you couldn’t during the contest.
► Step 5: Revise smart
Create a “Revision Sheet” with 100 key problems you’ve solved and want to reattempt.
Every 2-3 weeks, pick problems randomly and solve again without seeing solutions.
This trains your recall + improves your clarity.
Coding Projects:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
Here’s exactly how I’d approach it (I’ve taught this to 100s of students and followed it myself to land interviews at 3+ FAANGs):
► Step 1: Learn to Code (from scratch, even if you’re from non-CS background)
I helped my sister go from zero coding knowledge (she studied Biology and Electrical Engineering) to landing a job at Microsoft.
We started with:
- A simple programming language (C++, Java, Python — pick one)
- FreeCodeCamp on YouTube for beginner-friendly lectures
- Key rule: Don’t just watch. Code along with the video line by line.
Time required: 30–40 days to get good with loops, conditions, syntax.
► Step 2: Start with DSA before jumping to development
Why?
- 90% of tech interviews in top companies focus on Data Structures & Algorithms
- You’ll need time to master it, so start early.
Start with:
- Arrays → Linked List → Stacks → Queues
- You can follow the DSA videos on my channel.
- Practice while learning is a must.
► Step 3: Follow a smart topic order
Once you’re done with basics, follow this path:
1. Searching & Sorting
2. Recursion & Backtracking
3. Greedy
4. Sliding Window & Two Pointers
5. Trees & Graphs
6. Dynamic Programming
7. Tries, Heaps, and Union Find
Make revision notes as you go — note down how you solved each question, what tricks worked, and how you optimized it.
► Step 4: Start giving contests (don’t wait till you’re “ready”)
Most students wait to “finish DSA” before attempting contests.
That’s a huge mistake.
Contests teach you:
- Time management under pressure
- Handling edge cases
- Thinking fast
Platforms: LeetCode Weekly/ Biweekly, Codeforces, AtCoder, etc.
And after every contest, do upsolving — solve the questions you couldn’t during the contest.
► Step 5: Revise smart
Create a “Revision Sheet” with 100 key problems you’ve solved and want to reattempt.
Every 2-3 weeks, pick problems randomly and solve again without seeing solutions.
This trains your recall + improves your clarity.
Coding Projects:👇
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
ENJOY LEARNING 👍👍
❤2
Beginner’s Roadmap to Learn Data Structures & Algorithms
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
❤2
🔰 How to become a data scientist in 2025?
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data
❤4
Don't forget to check these 10 SQL projects with corresponding datasets that you could use to practice your SQL skills:
1. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
2. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
3. Social Media Analytics:
(https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels)
4. Financial Data Analysis:
(https://www.kaggle.com/datasets/nitindatta/finance-data)
5. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
6. Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data)
7. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
8. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
9. Supply Chain Management:
(https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis)
10. Inventory Management:
(https://www.kaggle.com/datasets?search=inventory+management)
Share this channel with your friends 🤝🤩
Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
ENJOY LEARNING 👍👍
1. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
2. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
3. Social Media Analytics:
(https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels)
4. Financial Data Analysis:
(https://www.kaggle.com/datasets/nitindatta/finance-data)
5. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
6. Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data)
7. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
8. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
9. Supply Chain Management:
(https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis)
10. Inventory Management:
(https://www.kaggle.com/datasets?search=inventory+management)
Share this channel with your friends 🤝🤩
Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
ENJOY LEARNING 👍👍
❤4
Website Development Roadmap – 2025
🔹 Stage 1: HTML – Learn the basics of web page structure.
🔹 Stage 2: CSS – Style and enhance web pages (Flexbox, Grid, Animations).
🔹 Stage 3: JavaScript (ES6+) – Add interactivity and dynamic features.
🔹 Stage 4: Git & GitHub – Manage code versions and collaborate.
🔹 Stage 5: Responsive Design – Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS).
🔹 Stage 6: UI/UX Basics – Understand user experience and design principles.
🔹 Stage 7: JavaScript Frameworks – Learn React.js, Vue.js, or Angular for interactive UIs.
🔹 Stage 8: Backend Development – Use Node.js, PHP, Python, or Ruby to
build server-side logic.
🔹 Stage 9: Databases – Work with MySQL, PostgreSQL, or MongoDB for data storage.
🔹 Stage 10: RESTful APIs & GraphQL – Create APIs for data communication.
🔹 Stage 11: Authentication & Security – Implement JWT, OAuth, and HTTPS best practices.
🔹 Stage 12: Full Stack Project – Build a fully functional website with both frontend and backend.
🔹 Stage 13: Testing & Debugging – Use Jest, Cypress, or other testing tools.
🔹 Stage 14: Deployment – Host websites using Netlify, Vercel, or cloud services.
🔹 Stage 15: Performance Optimization – Improve website speed (Lazy Loading, CDN, Caching).
📂 Web Development Resources
ENJOY LEARNING 👍👍
🔹 Stage 1: HTML – Learn the basics of web page structure.
🔹 Stage 2: CSS – Style and enhance web pages (Flexbox, Grid, Animations).
🔹 Stage 3: JavaScript (ES6+) – Add interactivity and dynamic features.
🔹 Stage 4: Git & GitHub – Manage code versions and collaborate.
🔹 Stage 5: Responsive Design – Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS).
🔹 Stage 6: UI/UX Basics – Understand user experience and design principles.
🔹 Stage 7: JavaScript Frameworks – Learn React.js, Vue.js, or Angular for interactive UIs.
🔹 Stage 8: Backend Development – Use Node.js, PHP, Python, or Ruby to
build server-side logic.
🔹 Stage 9: Databases – Work with MySQL, PostgreSQL, or MongoDB for data storage.
🔹 Stage 10: RESTful APIs & GraphQL – Create APIs for data communication.
🔹 Stage 11: Authentication & Security – Implement JWT, OAuth, and HTTPS best practices.
🔹 Stage 12: Full Stack Project – Build a fully functional website with both frontend and backend.
🔹 Stage 13: Testing & Debugging – Use Jest, Cypress, or other testing tools.
🔹 Stage 14: Deployment – Host websites using Netlify, Vercel, or cloud services.
🔹 Stage 15: Performance Optimization – Improve website speed (Lazy Loading, CDN, Caching).
📂 Web Development Resources
ENJOY LEARNING 👍👍
❤3