✅ DSA Roadmap for Coding Interviews 🧠💻

1️⃣ Start with the Basics
– Learn Time & Space Complexity
– Understand Big O notation

2️⃣ Master Arrays & Strings
– Sliding window, Two pointers, Prefix sum
– Practice problems like: Two Sum, Move Zeroes

3️⃣ Dive into Hashing
– Use HashMap/HashSet for fast lookups
– Problems: Longest Substring Without Repeat, Group Anagrams

4️⃣ Linked Lists
– Learn traversal, reversal, cycle detection
– Key problems: Detect Cycle, Merge Two Sorted Lists

5️⃣ Stacks & Queues
– Infix to postfix, parentheses validation, monotonic stack
– Problems: Valid Parentheses, Next Greater Element

6️⃣ Recursion & Backtracking
– Subsets, Permutations, N-Queens
– Key skill: build solution tree and backtrack correctly

7️⃣ Binary Search & Search Problems
– Classic problems: Search in Rotated Array, Koko Eating Bananas
– Understand upper/lower bounds

8️⃣ Trees & Binary Trees
– DFS, BFS, Inorder/Preorder/Postorder
– Problems: Lowest Common Ancestor, Diameter of Tree

9️⃣ Heaps & Priority Queues
– Top K elements, Min/Max heap use cases

🔟 Graphs
– BFS, DFS, Union-Find, Dijkstra’s
– Practice shortest path, connected components, cycle detection

1️⃣1️⃣ Dynamic Programming (DP)
– Start with 1D DP (Fibonacci, Climbing Stairs)
– Move to 2D DP (Knapsack, LCS, Grid Paths)

💡 Tip: Practice on LeetCode, Codeforces, GFG. Use patterns, not memorization.

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Every job interview has a "Do you have any questions?" part at the end.

But almost every candidate says "No questions from me" or doesn't ask good ones.

Here are 5 interview questions that you can ask to stand out and land offers:

1. I saw you did (XYZ) before joining
(COMPANY). What's your "why", and why did you decide to pursue a career here?

This question takes the interviewer out of the "interviewing" mindset and let's them talk more about their own journey. You can then mention how you relate to their journey, which creates empathy and a stronger connection with the interviewer.

2. If you looked back on this role a year later, what outcomes would indicate that this hire was successful?

This question allows you to see what they're looking for someone to accomplish in the role, and you can then answer their answer by showcasing how you'd do your best to achieve those outcomes.

3. What is the vision of the team in the next 6-12 months, and how would I contribute to having that vision come to life?

This question shows that you're forward-thinking and "in it to win it". You're also getting a deeper understanding of the goals your team has and how you'll play a part in achieving them.

4. What do you believe is the most important skill someone must have in this position?

This question allows you to identify the skill the interviewer wants to see in the candidate, and then you can turn the question back on them and share why you have that skills from your experiences.

5. What are next steps in the interview process, and when should I be hearing back?

This question gives you a timeline of how the interview process will go (if not shared already) and when you should follow-up if you don't hear back from the interviewer.

💰 Want to Build an App That Makes Money? Read This 🚀

Forget innovation. Focus on profit. Here's the strategy to build apps that actually earn:

🔥 4 Proven App Ideas That Work:
1. Solve a tiny, ignored problem.
2. Solve a big, common problem (even in crowded markets).
3. Use the “X for Y” formula (e.g., “Tinder for pet adoption”).
4. Build AI-powered tools using GPT models.

🎯 Follow the SLC Rule:
• Simple: 1–2 core features.
• Lovable: Great UI/UX (design matters!).
• Complete: Fully functional, not half-baked.

🛠️ Recommended Tech Stack:
• Frontend: Next.js + Shadcn UI + Tailwind CSS
• Backend: Supabase
• Analytics: PostHog
• Payments: Stripe

⚡ Use AI to Build Faster:
Tools like Bolt, V0, Cursor, and Lovable can generate code, UI, and workflows in minutes.

📅 1-Week App Launch Plan:
1. Outline with ChatGPT
2. Generate starter code with AI
3. Design with Mobbin/ Pageflows
4. Customize ship it!

📢 Marketing = Non-Negotiable:
• Be loud on Reddit, WhatsApp, Twitter, LinkedIn, TikTok, YouTube, Instagram or Telegram
• Use SEO metadata
• Start organic, then scale with ads

💼 Mindset Shift: Code like a founder. Think users, revenue, and growth—not just features.

📌 Double Tap ♥️ For More

𝐒𝐐𝐋 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰:

Join for more: https://news.1rj.ru/str/sqlanalyst

1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/

2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/

3. Foodie Fie
Subnoscription-based food content platform
Link: https://lnkd.in/gzB39qAT

4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv

5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf

6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG

7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7

8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8

✅ If you're serious about learning Web Development — follow this roadmap 🌐💻

1. Learn HTML basics (structure, elements, attributes) 📄
2. Master CSS (selectors, box model, flexbox, grid) 🎨
3. Understand responsive design: media queries, mobile-first approach 📱
4. Learn JavaScript fundamentals: variables, loops, functions, DOM manipulation 🖥️
5. Get familiar with version control using Git (repositories, commits, branches) 🗂️
6. Study JavaScript ES6+ features: arrow functions, promises, async/await 🚀
7. Learn about the Document Object Model (DOM) and how to manipulate it 📜
8. Explore front-end frameworks: React, Vue.js, or Angular 🔧
9. Understand state management concepts (Redux, Context API) 📊
10. Learn about RESTful APIs and how to make API requests (fetch, Axios) 🌐
11. Dive into back-end development: choose Node.js with Express or Python with Flask/Django 🛠️
12. Understand databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB) 💾
13. Learn about authentication and authorization (JWT, OAuth) 🔑
14. Explore deployment options: Heroku, Vercel, Netlify 🚀
15. Get familiar with web security basics: HTTPS, CORS, XSS, CSRF 🛡️
16. Build full-stack projects (CRUD apps, e-commerce site) 🏗️
17. Learn about testing frameworks: Jest for JavaScript or PyTest for Python 🧪
18. Understand performance optimization techniques (lazy loading, minification) ⚡
19. Create a personal portfolio website to showcase your projects 🌟
20. Stay updated with web technologies: follow blogs, podcasts, and communities 💬
21. Contribute to open-source projects to gain experience and visibility 🌍
22. Network with other developers through meetups or online forums 🤝
23. Apply for internships or junior developer positions to gain real-world experience 🎯

Tip: Build projects that interest you—this keeps motivation high!

💬 Tap ❤️ for more!

Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape

🔘Pro is currently the #1 open-source model worldwide
🔘Lite (2B parameters) outperforms Sora v1.
🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21.

Useful links
🔘Full leaderboard: LM Arena
🔘Kandinsky 5.0 details: technical report
🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

Bookmark these sites FOREVER!!!

❯ HTML ➟ learn-html
❯ CSS ➟ css-tricks
❯ JavaScript ➟ javanoscript .info
❯ Python ➟ realpython
❯ C ➟ learn-c
❯ C++ ➟ fluentcpp
❯ Java ➟ baeldung
❯ SQL ➟ sqlbolt
❯ Go ➟ learn-golang
❯ Kotlin ➟ studytonight
❯ Swift ➟ codewithchris
❯ C# ➟ learncs
❯ PHP ➟ learn-php
❯ DSA ➟ techdevguide .withgoogle

🚀 Roadmap to Master DSA (Data Structures  Algorithms) in 60 Days! 📚💻

📅 Week 1–2: Foundations 
🔹 Day 1–3: Time  Space Complexity 
🔹 Day 4–7: Recursion basics  practice 
🔹 Day 8–10: Arrays – operations, sliding window 
🔹 Day 11–14: Strings – patterns, hashing, two pointers

📅 Week 3–4: Core Data Structures 
🔹 Day 15–17: Linked Lists – single, double, reverse 
🔹 Day 18–20: Stacks  Queues – using arrays  linked lists 
🔹 Day 21–24: Trees – traversal, height, BST 
🔹 Day 25–28: Binary Search Trees  Heaps

📅 Week 5–6: Algorithms  Graphs 
🔹 Day 29–31: Sorting – bubble, merge, quick 
🔹 Day 32–35: Binary Search – on arrays  answer 
🔹 Day 36–40: Backtracking – N-Queens, Sudoku 
🔹 Day 41–44: Graphs – BFS, DFS, adjacency list/matrix 
🔹 Day 45–48: Dijkstra, Topological Sort, Union-Find

📅 Week 7–8: Advanced Concepts 
🔹 Day 49–52: Dynamic Programming – Fibonacci, LCS, LIS 
🔹 Day 53–55: Greedy – activity selection, coin change 
🔹 Day 56–58: Tries, Segment Trees (basic) 
🔹 Day 59–60: Practice full mock tests  revise

💬 Tap ❤️ for more!

Useful Git Commands📝👨🏻‍💻

React ❤️ for more like this

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗕𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 😍

Roadmap to land your dream job in top product-based companies

𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:-
- 90-Day Placement Plan
- Tech & Non-Tech Career Path
- Interview Preparation Tips
- Live Q&A

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 

https://pdlink.in/3Ltb3CE

Date & Time:- 06th January 2026 , 7PM

Artificial Intelligence (AI) Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing

Best Resources to learn ML & AI 👇

Learn Python for Free

Prompt Engineering Course

Prompt Engineering Guide

Data Science Course

Google Cloud Generative AI Path

Machine Learning with Python Free Course

Machine Learning Free Book

Artificial Intelligence WhatsApp channel

Hands-on Machine Learning

Deep Learning Nanodegree Program with Real-world Projects

AI, Machine Learning and Deep Learning

Like this post for more roadmaps ❤️

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ENJOY LEARNING👍👍