Coding Interview Resources

Python Interview Questions with Answers

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✅ Top GitHub Repositories to Learn Coding (FREE) 🧑‍💻⭐ 1️⃣ 📘 JavaScript Algorithms github.com/trekhleb/javascript-algorithms – 100+ algorithms & data structures in JS with explanations – Great for interviews and DSA prep 2️⃣ 📗 30 Days of JavaScript github.com/Asabeneh/30-Days-Of-JavaScript – Learn JS step-by-step from basics to DOM & OOP – Ideal for self-paced learners 3️⃣ 📙 System Design Primer github.com/donnemartin/system-design-primer – Learn how to design scalable systems – Must-read for backend & interview prep 4️⃣ 📒 Awesome Python github.com/vinta/awesome-python – Curated list of Python libraries, tools, and resources – Explore everything from web dev to ML 5️⃣ 📕 Frontend Developer Roadmap github.com/EnoahNetz/Frontend-Developer-Interview-Preparation – Full frontend prep with HTML, CSS, JS, React – Also includes interview tips & resources 6️⃣ 📓 Developer Roadmap github.com/kamranahmedse/developer-roadmap – Visual roadmap for frontend, backend, DevOps – Helps you plan your learning path 7️⃣ 📔 Free Programming Books github.com/EbookFoundation/free-programming-books – 1000+ books in 30+ languages – Covers all major programming topics 💡 Pro Tip: Star and fork useful repos. Use GitHub like your personal learning library. 💬 Tap ❤️ for more!

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🚀 Top Coding Interview Concepts – Part 2 💻 11. Stack  A linear data structure that follows the LIFO (Last In, First Out) principle. The last element added is the first one removed. 12. Queue  A linear data structure that follows the FIFO (First In, First Out) principle. The first element added is the first one removed. 13. Hash Table (Hash Map)  A data structure that stores key-value pairs and provides fast insertion, deletion, and lookup using a hash function. 14. Tree  A hierarchical data structure consisting of nodes connected by edges, commonly used to represent hierarchical relationships. 15. Binary Search Tree (BST)  A special type of binary tree where values smaller than the root are stored on the left, and larger values on the right. 16. Graph  A collection of nodes (vertices) connected by edges, used to model relationships such as social networks or maps. 17. Sorting  The process of arranging data in a specific order, such as ascending or descending, to improve searching and processing. 18. Searching  The process of finding a specific element within a collection of data using algorithms like Linear Search or Binary Search. 19. Time Complexity  A measure of how the execution time of an algorithm grows as the input size increases, commonly represented using Big-O notation. 20. Space Complexity  A measure of the amount of memory an algorithm requires relative to the input size, helping evaluate memory efficiency. 💬 Double Tap ❤️ for Part 3!

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Coding and Aptitude Round before interview Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking. Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round. Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you. Resources for Prep: For algorithms and data structures prep,Leetcode and Hackerrank are good resources. For aptitude prep, you can refer to IndiaBixand Practice Aptitude. With respect to data science challenges, practice well on GLabs and Kaggle. Brilliant is an excellent resource for tricky math and statistics questions. For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself. Things to Note: Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do! In case, you are finished with the test before time, recheck your answers and then submit. Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

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10 Steps to Landing a High Paying Job in Data Analytics 1. Learn SQL - joins & windowing functions is most important 2. Learn Excel- pivoting, lookup, vba, macros is must 3. Learn Dashboarding on POWER BI/ Tableau 4. ⁠Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries 5. ⁠Know basics of descriptive statistics 6. ⁠With AI/ copilot integrated in every tool, know how to use it and add to your projects 7. ⁠Have hands on any 1 cloud platform- AZURE/AWS/GCP 8. ⁠WORK on atleast 2 end to end projects and create a portfolio of it 9. ⁠Prepare an ATS friendly resume & start applying 10. ⁠Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those. Give more interview to boost your chances through consistent practice & feedback 😄👍

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🚀 Coding Interview Concepts – Part 1 💻 1. Variables Named containers used to store data that can change during program execution. 2. Data Types Define the kind of data a variable can hold, such as integers, strings, or booleans. 3. Operators Symbols used to perform operations like addition +, comparison ==, or logical checks &&. 4. Conditional Statements Control the flow of a program by executing different code based on conditions. Examples: if, else, switch 5. Loops Execute the same block of code repeatedly until a condition is met. Examples: for, while, do-while 6. Functions Reusable blocks of code designed to perform a specific task. They improve code organization and reduce repetition. 7. Recursion A technique where a function calls itself to solve a problem by breaking it into smaller subproblems. 8. Arrays A collection of elements of the same data type stored in contiguous memory locations and accessed by index. 9. Strings A sequence of characters used to represent text, such as names, messages, or sentences. 10. Linked List A linear data structure where each element (node) contains data and a pointer to the next node, allowing efficient insertion and deletion. 💬 Double Tap ❤️ for Part 2! ----- 1.23 ₽ · /balance_help

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