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Last active July 23, 2024 03:45
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12-week study plan for leetcode, generated by claude
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12weeks.md

LeetCode Study Plan

Overview

This study plan is designed to help you systematically improve your problem-solving skills and prepare for technical interviews using LeetCode.

Goals

  • Develop a strong foundation in data structures and algorithms
  • Improve problem-solving skills
  • Prepare for technical interviews

Schedule

Week 1-2: Arrays and Strings

  • Study array manipulation techniques
  • Practice string problems
  • Solve 10-15 easy problems

Week 3-4: Linked Lists and Stacks/Queues

  • Implement basic linked list operations
  • Understand stack and queue concepts
  • Solve 10-15 medium problems

Week 5-6: Trees and Graphs

  • Study tree traversal algorithms
  • Learn basic graph algorithms (BFS, DFS)
  • Solve 10-15 medium to hard problems

Week 7-8: Dynamic Programming

  • Understand DP concepts
  • Practice classic DP problems
  • Solve 10-15 medium to hard problems

Week 9-10: Sorting and Searching

  • Review common sorting algorithms
  • Practice binary search variations
  • Solve 10-15 medium problems

Week 11-12: Review and Advanced Topics

  • Revisit challenging problems
  • Study advanced topics (e.g., Trie, Union Find)
  • Solve mixed difficulty problems

Resources

  • LeetCode Premium (if available)
  • Algorithm design books
  • Online tutorials and video explanations

Progress Tracking

  • Keep a log of solved problems
  • Review and analyze your solutions regularly
  • Participate in LeetCode contests

Notes

  • Adjust the plan based on your progress and areas that need more focus
  • Consistency is key - aim for daily practice
  • Don't hesitate to seek help or explanations when stuck
Raw
dsa.md

Data Structures and Algorithms Study Plan

Overview

This study plan is designed to build a solid foundation in data structures and algorithms, essential for problem-solving and software development.

Goals

  • Understand fundamental data structures
  • Learn key algorithms and their implementations
  • Develop problem-solving skills
  • Prepare for technical interviews and coding challenges

Schedule

Week 1-2: Introduction and Basic Data Structures

  • Study Big O notation and time/space complexity
  • Learn and implement Arrays and Strings
  • Understand and implement Linked Lists
  • Practice problems related to these structures

Week 3-4: Stacks, Queues, and Hash Tables

  • Study and implement Stacks and Queues
  • Learn about Hash Tables and their applications
  • Implement a basic Hash Table
  • Solve problems using these data structures

Week 5-6: Trees and Heaps

  • Learn about Binary Trees and Binary Search Trees
  • Implement tree traversal algorithms (in-order, pre-order, post-order)
  • Study and implement Heaps (min-heap, max-heap)
  • Practice tree and heap-related problems

Week 7-8: Graphs

  • Understand Graph representations (adjacency list, adjacency matrix)
  • Learn and implement Graph traversal algorithms (BFS, DFS)
  • Study shortest path algorithms (Dijkstra's, Bellman-Ford)
  • Practice graph problems

Week 9-10: Sorting and Searching Algorithms

  • Study and implement basic sorting algorithms (Bubble, Selection, Insertion)
  • Learn and implement advanced sorting algorithms (Merge Sort, Quick Sort)
  • Understand and implement Binary Search
  • Practice sorting and searching problems

Week 11-12: Dynamic Programming and Greedy Algorithms

  • Learn the concept of Dynamic Programming
  • Solve classic DP problems (e.g., Fibonacci, Knapsack)
  • Understand Greedy Algorithms and their applications
  • Practice DP and Greedy problems

Week 13-14: Advanced Data Structures

  • Study Trie data structure
  • Learn about Union-Find (Disjoint Set) data structure
  • Understand Segment Trees and Fenwick Trees
  • Implement and practice problems with these structures

Week 15-16: Review and Advanced Topics

  • Revisit challenging concepts
  • Study string algorithms (KMP, Rabin-Karp)
  • Learn about NP-completeness and approximation algorithms
  • Solve mixed problems to reinforce learning

Resources

  • Books: "Introduction to Algorithms" by CLRS, "Algorithms" by Robert Sedgewick
  • Online platforms: LeetCode, HackerRank, Coursera, edX
  • YouTube channels: MIT OpenCourseWare, mycodeschool, Abdul Bari

Progress Tracking

  • Implement each data structure and algorithm from scratch
  • Solve at least 5 problems for each topic
  • Create a GitHub repository to store implementations and solutions

Notes

  • Focus on understanding the underlying concepts, not just memorization
  • Practice implementing algorithms without referring to notes
  • Regularly review and revise previously learned topics
  • Participate in coding contests to apply your knowledge
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