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Coding Interview Resources

Coding Interview Resources

الذهاب إلى القناة على Telegram

This channel contains the free resources and solution of coding problems which are usually asked in the interviews. Managed by: @love_data

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Coding Interview Resources

تُعد قناة Coding Interview Resources (@crackingthecodinginterview) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 52 259 مشتركاً، محتلاً المرتبة 2 474 في فئة التكنولوجيات والتطبيقات والمرتبة 6 728 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 52 259 مشتركاً.

بحسب آخر البيانات بتاريخ 30 أغسطس, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 24، وفي آخر 24 ساعة بمقدار 20، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 1.80‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 0.75‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 943 مشاهدة. وخلال اليوم الأول يجمع عادةً 392 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 2.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل array, stack, algorithm, programming, sort.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
This channel contains the free resources and solution of coding problems which are usually asked in the interviews. Managed by: @love_data

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 31 أغسطس, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

52 259
المشتركون
+2024 ساعات
+217 أيام
+2430 أيام
أرشيف المشاركات
🚀 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 👨‍💻

𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁
𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁!😍 Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! 🚀 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4ld6F7Y No more flipping through tabs & tutorials—just pin this cheat sheet and analyze data like a pro!✅️

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Getting job offers as a developer involves several steps:👨‍💻🚀 1. Build a Strong Portfolio: Create a portfolio of projects that showcase your skills. Include personal projects, open-source contributions, or freelance work. This demonstrates your abilities to potential employers.👨‍💻 2. Enhance Your Skills: Stay updated with the latest technologies and trends in your field. Consider taking online courses, attending workshops, or earning certifications to bolster your skills.🚀 3. Network: Attend industry events, conferences, and meetups to connect with professionals in your field. Utilize social media platforms like LinkedIn to build a professional network.🔥 4. Resume and Cover Letter: Craft a tailored resume and cover letter for each job application. Highlight relevant skills and experiences that match the job requirements.📇 5. Job Search Platforms: Utilize job search websites like LinkedIn, Indeed, Glassdoor, and specialized platforms like Stack Overflow Jobs, GitHub Jobs, or AngelList for tech-related positions. 🔍 6. Company Research: Research companies you're interested in working for. Customize your application to show your genuine interest in their mission and values.🕵️‍♂️ 7. Prepare for Interviews: Be ready for technical interviews. Practice coding challenges, algorithms, and data structures. Also, be prepared to discuss your past projects and problem-solving skills.📝 8. Soft Skills: Develop your soft skills like communication, teamwork, and problem-solving. Employers often look for candidates who can work well in a team and communicate effectively.💻 9. Internships and Freelancing: Consider internships or freelancing opportunities to gain practical experience and build your resume. 🏠 10. Personal Branding: Maintain an online presence by sharing your work, insights, and thoughts on platforms like GitHub, personal blogs, or social media. This can help you get noticed by potential employers.👦 11. Referrals: Reach out to your network and ask for referrals from people you know in the industry. Employee referrals are often highly valued by companies.🌈 12. Persistence: The job search process can be challenging. Don't get discouraged by rejections. Keep applying, learning, and improving your skills.💯 13. Negotiate Offers: When you receive job offers, negotiate your salary and benefits. Research industry standards and be prepared to discuss your expectations.📉 Remember that the job search process can take time, so patience is key. By focusing on these steps and continuously improving your skills and network, you can increase your chances of receiving job offers as a developer.

𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 - Python Programming - Data Analytics - Generative AI - Machine L
𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 - Python Programming - Data Analytics  - Generative AI - Machine Learning  - Data Science  - SQL 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/41VIuSA Enroll Now & Get a course completion certificate🎓

5 Steps to learn DSA 👆
+5
5 Steps to learn DSA 👆

𝗟𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍 Want to break into AI, UI/UX, or proje
𝗟𝗲𝗮𝗿𝗻 𝗔𝗜, 𝗗𝗲𝘀𝗶𝗴𝗻 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍 Want to break into AI, UI/UX, or project management? 🚀 These 5 beginner-friendly FREE courses will help you develop in-demand skills and boost your resume in 2025!🎊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4iV3dNf ✨ No cost, no catch—just pure learning from anywhere!

DSA Roadmap 👆
DSA Roadmap 👆

Python vs C++ vs Java 😂
Python vs C++ vs Java 😂

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍 - Capgemini - Infosys - KPMG - Genpact - JP Morgan Qualification :-
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There's a tool that makes $1,000 a day on currency pairs without your input. ❗️ If you had just followed Jay signals last wee
There's a tool that makes $1,000 a day on currency pairs without your input. ❗️ If you had just followed Jay signals last week, you would have already made $7,000. ❗️ 87% accurate entries - even a beginner makes money without experience. ❗️ In the last 30 days, people with a $500 deposit have maxed it out to $4,800. How does it work? Jay, with the help of a bot, finds the right trade entry points and makes money from it. You just repeat her trades and come out in the plus side. 🚀 Signals are still free - get in first! 📲 Sign up before they close your access:👇 t.me/jaymo_trader t.me/jaymo_trader t.me/jaymo_trader

Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months ### Week 1: Introduction to Python Day 1-2: Basics of Python - Python setup (installation and IDE setup) - Basic syntax, variables, and data types - Operators and expressions Day 3-4: Control Structures - Conditional statements (if, elif, else) - Loops (for, while) Day 5-6: Functions and Modules - Function definitions, parameters, and return values - Built-in functions and importing modules Day 7: Practice Day - Solve basic problems on platforms like HackerRank or LeetCode ### Week 2: Advanced Python Concepts Day 8-9: Data Structures in Python - Lists, tuples, sets, and dictionaries - List comprehensions and generator expressions Day 10-11: Strings and File I/O - String manipulation and methods - Reading from and writing to files Day 12-13: Object-Oriented Programming (OOP) - Classes and objects - Inheritance, polymorphism, encapsulation Day 14: Practice Day - Solve intermediate problems on coding platforms ### Week 3: Introduction to Data Structures Day 15-16: Arrays and Linked Lists - Understanding arrays and their operations - Singly and doubly linked lists Day 17-18: Stacks and Queues - Implementation and applications of stacks - Implementation and applications of queues Day 19-20: Recursion - Basics of recursion and solving problems using recursion - Recursive vs iterative solutions Day 21: Practice Day - Solve problems related to arrays, linked lists, stacks, and queues ### Week 4: Fundamental Algorithms Day 22-23: Sorting Algorithms - Bubble sort, selection sort, insertion sort - Merge sort and quicksort Day 24-25: Searching Algorithms - Linear search and binary search - Applications and complexity analysis Day 26-27: Hashing - Hash tables and hash functions - Collision resolution techniques Day 28: Practice Day - Solve problems on sorting, searching, and hashing ### Week 5: Advanced Data Structures Day 29-30: Trees - Binary trees, binary search trees (BST) - Tree traversals (in-order, pre-order, post-order) Day 31-32: Heaps and Priority Queues - Understanding heaps (min-heap, max-heap) - Implementing priority queues using heaps Day 33-34: Graphs - Representation of graphs (adjacency matrix, adjacency list) - Depth-first search (DFS) and breadth-first search (BFS) Day 35: Practice Day - Solve problems on trees, heaps, and graphs ### Week 6: Advanced Algorithms Day 36-37: Dynamic Programming - Introduction to dynamic programming - Solving common DP problems (e.g., Fibonacci, knapsack) Day 38-39: Greedy Algorithms - Understanding greedy strategy - Solving problems using greedy algorithms Day 40-41: Graph Algorithms - Dijkstra’s algorithm for shortest path - Kruskal’s and Prim’s algorithms for minimum spanning tree Day 42: Practice Day - Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms ### Week 7: Problem Solving and Optimization Day 43-44: Problem-Solving Techniques - Backtracking, bit manipulation, and combinatorial problems Day 45-46: Practice Competitive Programming - Participate in contests on platforms like Codeforces or CodeChef Day 47-48: Mock Interviews and Coding Challenges - Simulate technical interviews - Focus on time management and optimization Day 49: Review and Revise - Go through notes and previously solved problems - Identify weak areas and work on them ### Week 8: Final Stretch and Project Day 50-52: Build a Project - Use your knowledge to build a substantial project in Python involving DSA concepts Day 53-54: Code Review and Testing - Refactor your project code - Write tests for your project Day 55-56: Final Practice - Solve problems from previous contests or new challenging problems Day 57-58: Documentation and Presentation - Document your project and prepare a presentation or a detailed report Day 59-60: Reflection and Future Plan - Reflect on what you've learned - Plan your next steps (advanced topics, more projects, etc.) Best DSA RESOURCES: https://topmate.io/coding/886874 Credits: https://t.me/free4unow_backup ENJOY LEARNING 👍👍

𝗬𝗼𝘂𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍 Want to break into Artificial Intel
𝗬𝗼𝘂𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍 Want to break into Artificial Intelligence and work with cutting-edge technologies?👋 This FREE roadmap will guide you through everything you need to become an AI Engineer in 2025!🎊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4iA6aTE Build Real-World AI Projects & stand out from the crowd!✅️

Do you know these symbols?
Do you know these symbols?

𝐁𝐞𝐜𝐨𝐦𝐞 𝐀 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬 😍 Learn Data Analytics, Data Science & AI Curriculum designed a
𝐁𝐞𝐜𝐨𝐦𝐞 𝐀 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂𝐬 😍  Learn Data Analytics, Data Science & AI Curriculum designed and taught by Alumni from IITs Learn by doing, build Industry level projects 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐞𝐬:-  🙌100% Job Assistance 🎓450+ Partner Companies 💻50+ Practice Interviews 𝐁𝐨𝐨𝐤 𝐚 𝟏:𝟏 𝐅𝐑𝐄𝐄 𝐂𝐨𝐮𝐧𝐬𝐞𝐥𝐢𝐧𝐠 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 👇:- https://bit.ly/4g3kyT6 ( Limited Slots )

Most Asked Interview Questions with Answers 💻✅
+9
Most Asked Interview Questions with Answers 💻✅

𝟱 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍 Looking
𝟱 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍 Looking to break into data analytics but don’t know where to start?👋 🚀 The demand for data professionals is skyrocketing in 2025, & 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗮 𝗱𝗲𝗴𝗿𝗲𝗲 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱!🚨 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4kLxe3N 🔗 Start now and transform your career for FREE!

🌻 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗕𝗶𝗴 𝗢 𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻! O(1) - Constant Time: Simple tasks that take the same amount of time no matter how much data you have, like finding an item in a list by its position. O(log n) - Logarithmic Time: Tasks that take less time as the data grows, like finding an item in a sorted list by repeatedly dividing it in half. O(n) - Linear Time: Tasks that take more time as the data grows, like counting all items in a list by checking each one. O(n log n) - Linearithmic Time: Tasks that get a bit slower as the data grows, like sorting a list using efficient methods such as merge sort or quick sort. O(n²) - Quadratic Time: Tasks that get noticeably slower as the data grows, like sorting a list using simpler methods like bubble sort or finding all pairs in a list. O(2^n) - Exponential Time: Tasks that get much slower as the data grows, like finding all subsets of a set or solving complex problems like the traveling salesman using a basic approach. O(n!) - Factorial Time: Tasks that get extremely slow as the data grows, like solving problems that involve checking every possible arrangement of items.

A programmer's life summed up in one meme 😄😂
A programmer's life summed up in one meme 😄😂

𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀!😍 Want to boost your data skill
𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀!😍 Want to boost your data skills without spending a dime? These FREE SQL courses will take you from beginner to expert, whether you’re an aspiring Data Analyst, Data Scientist, or Backend Developer!📊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4l2q2Ay Start Learning Today ✅️