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Coding Interview Resources

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

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  • 15 авг.✅ Top 50 DSA (Data Structures & Algorithms) Interview Questions 📚⚙️ 1. What is a Data Structure? 2. What are the different types of data structures? 3. What is the difference between Array and Linked List? 4. How does a Stack work? 5. What is a Queue? Difference between Queue and Deque? 6. What is a Priority Queue? 7. What is a Hash Table and how does it work? 8. What is the difference between HashMap and HashSet? 9. What are Trees? Explain Binary Tree. 10. What is a Binary Search Tree (BST)? 11. What is the difference between BFS and DFS? 12. What is a Heap? 13. What is a Trie? 14. What is a Graph? 15. Difference between Directed and Undirected Graph? 16. What is the time complexity of common operations in arrays and linked lists? 17. What is recursion? 18. What are base case and recursive case? 19. What is dynamic programming? 20. Difference between Memoization and Tabulation? 21. What is the Sliding Window technique? 22. Explain Two-Pointer technique. 23. What is the Binary Search algorithm? 24. What is the Merge Sort algorithm? 25. What is the Quick Sort algorithm? 26. Difference between Merge Sort and Quick Sort? 27. What is Insertion Sort and how does it work? 28. What is Selection Sort? 29. What is Bubble Sort and its drawbacks? 30. What is the time and space complexity of sorting algorithms? 31. What is Backtracking? 32. Explain the N-Queens Problem. 33. What is the Kadane's Algorithm? 34. What is Floyd’s Cycle Detection Algorithm? 35. What is the Union-Find (Disjoint Set) algorithm? 36. What are topological sorting and its uses? 37. What is Dijkstra's Algorithm? 38. What is Bellman-Ford Algorithm? 39. What is Kruskal’s Algorithm? 40. What is Prim’s Algorithm? 41. What is Longest Common Subsequence (LCS)? 42. What is Longest Increasing Subsequence (LIS)? 43. What is a Palindrome Substring problem? 44. What is the difference between greedy and dynamic programming? 45. What is Big-O notation? 46. What is the difference between time and space complexity? 47. How to find the time complexity of a recursive function? 48. What are amortized time complexities? 49. What is tail recursion? 50. How do you approach solving a coding problem in interviews? 💬 Tap ❤️ for the detailed answers!1,41%
  • 14 авг.Creating a data science and machine learning project involves several steps, from defining the problem to deploying the model. Here is a general outline of how you can create a data science and ML project: 1. Define the Problem: Start by clearly defining the problem you want to solve. Understand the business context, the goals of the project, and what insights or predictions you aim to derive from the data. 2. Collect Data: Gather relevant data that will help you address the problem. This could involve collecting data from various sources, such as databases, APIs, CSV files, or web scraping. 3. Data Preprocessing: Clean and preprocess the data to make it suitable for analysis and modeling. This may involve handling missing values, encoding categorical variables, scaling features, and other data cleaning tasks. 4. Exploratory Data Analysis (EDA): Perform exploratory data analysis to understand the data better. Visualize the data, identify patterns, correlations, and outliers that may impact your analysis. 5. Feature Engineering: Create new features or transform existing features to improve the performance of your machine learning model. Feature engineering is crucial for building a successful ML model. 6. Model Selection: Choose the appropriate machine learning algorithm based on the problem you are trying to solve (classification, regression, clustering, etc.). Experiment with different models and hyperparameters to find the best-performing one. 7. Model Training: Split your data into training and testing sets and train your machine learning model on the training data. Evaluate the model's performance on the testing data using appropriate metrics. 8. Model Evaluation: Evaluate the performance of your model using metrics like accuracy, precision, recall, F1-score, ROC-AUC, etc. Make sure to analyze the results and iterate on your model if needed. 9. Deployment: Once you have a satisfactory model, deploy it into production. This could involve creating an API for real-time predictions, integrating it into a web application, or any other method of making your model accessible. 10. Monitoring and Maintenance: Monitor the performance of your deployed model and ensure that it continues to perform well over time. Update the model as needed based on new data or changes in the problem domain.0,73%
  • 12 авг.🚀 Top Coding Interview Concepts – Part 5 💻🔥 41. API → A set of rules and protocols that allows different software applications to communicate with each other. 42. REST API → An API architecture that uses HTTP methods and resources to enable communication between client and server. 43. HTTP Methods → Actions used to interact with resources through HTTP. Example: GET → retrieve data, POST → create data, PUT → update data, DELETE → remove data. 44. JSON → A lightweight text-based format commonly used to exchange structured data between applications. Example: {"name": "John", "age": 25} 45. Authentication → The process of verifying who a user or system is. Example: Logging in with a username and password. 46. Authorization → The process of determining what an authenticated user is allowed to access or do. Example: An admin can delete users, while a regular user cannot. 47. JWT (JSON Web Token) → A compact token format commonly used to securely transmit claims between systems and authenticate API requests. 48. Session → Information maintained by a server or application to keep track of a user's interaction over a period of time. Example: Staying logged in while navigating between pages. 49. Cookie → A small piece of data stored by a browser and sent with requests to help websites remember information about a user or session. 50. Cache → Temporary storage used to keep frequently accessed data so it can be retrieved faster. Example: A browser caches images so they load faster when you revisit a website. 💬 Double Tap ❤️ for Part 6!0,50%
  • 8 авг.🚀 Complete SQL Roadmap 🗄🔥 🧠 STEP 1: Learn SQL Basics ✔ What is SQL? ✔ Databases & Tables ✔ SELECT Statement ✔ WHERE Clause ✔ ORDER BY 🛠 Databases to Practice: ✔ MySQL ✔ PostgreSQL ✔ SQL Server 📊 STEP 2: Learn Filtering & Aggregation ✔ DISTINCT ✔ LIMIT & TOP ✔ COUNT, SUM, AVG ✔ MIN & MAX ✔ GROUP BY & HAVING ⚡ STEP 3: Master SQL JOINS ✔ INNER JOIN ✔ LEFT JOIN ✔ RIGHT JOIN ✔ FULL JOIN ✔ SELF JOIN 🛠 Concepts to Learn: ✔ Primary Key ✔ Foreign Key ✔ Relationships 📈 STEP 4: Learn Advanced SQL ✔ Subqueries ✔ Common Table Expressions (CTEs) ✔ CASE WHEN ✔ UNION & UNION ALL ✔ EXISTS & IN 🔥 STEP 5: Learn Window Functions ✔ ROW_NUMBER() ✔ RANK() ✔ DENSE_RANK() ✔ LEAD() & LAG() ✔ PARTITION BY 🧠 STEP 6: Learn Database Design ✔ Normalization ✔ Schema Design ✔ Indexing ✔ Constraints ✔ Data Integrity ☁️ STEP 7: Learn SQL Optimization ✔ Query Optimization ✔ Execution Plans ✔ Index Optimization ✔ Performance Tuning 🛠 Tools to Learn: ✔ DBeaver ✔ pgAdmin ✔ MySQL Workbench 📂 STEP 8: Build Real SQL Projects ✔ Sales Database Analysis ✔ Employee Management System ✔ E-commerce Database ✔ Customer Analytics ✔ Inventory Management 💡 SQL Notes: https://whatsapp.com/channel/0029VbCyzS02ZjCwoShXXc2j 💬 Tap ❤️ if this helped you!0,47%
  • 7 авг.🚀 𝗜𝗕𝗠 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🎓 Upgrade your tech skills with 100% FREE IBM certification courses and build a strong foundation in AI, Data Science, Cloud Computing, SQL, Python, and Machine Learning. 🎯 Perfect For 🎓 Students & Freshers 👨‍💻 Software Developers 📊 Data Analysts 🤖 AI & Data Science Aspirants 💼 Working Professionals 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- https://pdlink.in/45KgqDR 🔥 Start learning today and prepare yourself for high-paying opportunities in the tech industry!0,31%
  • 20 июн. 2025 г.без подписи0,18%
  • 14 авг.📊 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 | 𝟱 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 🚀 Learning Data Analytics? Don't stop with tutorials — build real projects that you can showcase on your resume and portfolio! 💻 🔥 Practice with 5 Hands-On Projects covering: 🗄️ SQL 📊 Excel 📈 Tableau 📉 Power BI 🔗𝗟𝗶𝗻𝗸 👇:- https://pdlink.in/45LLDH7 🎓 Perfect for Students | Freshers | Data Analyst Aspirants | Beginners0,14%
  • 10 авг.🚀 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 📊🔥 Build in-demand Data Analytics skills with Microsoft and strengthen your resume with FREE learning opportunities. ✅ Beginner-Friendly ✅ Learn at Your Own Pace ✅ Build Job-Ready Data Skills ✅ Improve Your Resume & LinkedIn Profile ✅ Prepare for Data Analyst & BI Careers 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- https://pdlink.in/4hXL4Ru 🔥 Start learning today and take your first step toward a career in Data Analytics & Business Intelligence0,13%
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  • 8 авг.🚀 𝗧𝗼𝗽 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 📊 💼 Companies hiring Power BI professionals include: Microsoft, Deloitte, Accenture, Capgemini, TCS, Infosys, Cognizant, EY, PwC, KPMG, IBM, Wipro, and many more. ✅ Frequently Asked Interview Questions ✅ Beginner to Advanced Level Coverage ✅ Improve Your Problem-Solving Skills ✅ Build Interview Confidence ✅ Prepare for Top MNC Hiring Drives 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4xqxg6v 🔥 Master Power BI interview concepts and take one step closer to landing your dream Data Analytics job!0,12%
  • 10 авг.If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc. Yes, you might hear a lot about them or some other trending technology of the year...but guess what! Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy. Instead, here are basic skills that will get you further than mastering any framework: 𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML. You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability 𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning. 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks. You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/ 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process. 𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚: Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently. You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai I love frameworks and libraries, and they can make anyone's job easier. But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 All the best 👍👍0,12%
  • 7 авг.🚀 𝗙𝗥𝗘𝗘 𝗙𝗿𝗲𝘀𝗵𝗲𝗿 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗿𝗶𝘃𝗲 | 𝗧𝗲𝗰𝗵 𝗥𝗼𝗹𝗲𝘀 𝗨𝗽 𝘁𝗼 ₹𝟭𝟮 𝗟𝗣𝗔!🔥 Internship + Pre-Placement Offer 💼 Company: GoComet 💰 Stipend: ₹30,000–35,000/Month 🚀 PPO: Up to ₹12 LPA 📍 Assessment Centres: Pune | Hyderabad | Noida | Chennai | Bangalore 🔗 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 👇: Full Stack Intern:- https://pdlink.in/4z3vF8o AI First SDET Interns :- https://pdlink.in/4hS1Am2 ⏳ Limited Hiring Slots Available0,11%