Coding Interview Resources

Python vs C++ vs Java 😂

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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 👍👍

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Do you know these symbols?

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🌻 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 😄😂

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Tech interviews ask candidates to invert binary trees while their real job is 90% figuring out why a 3rd-party API returns null sometimes.

Rest API in a nutshell

Algorithms for Coding Interviews 👆

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Technologies used by Netflix 👆

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