To-Do: Daily DSA

30 Sep, 2024

I prompted gpt to make me learning plan for 8 weeks learning DSA

Certainly! Given your 3 years of programming experience, you likely have a solid foundation to build upon. This 8-week comprehensive plan will guide you through Data Structures and Algorithms (DSA) systematically, dedicating 5 days a week with 2 hours each day. The plan balances theory, implementation, and practice to ensure a deep understanding and proficiency.

Overview

• Duration: 8 Weeks
• Frequency: 5 Days/Week
• Daily Commitment: 2 Hours
• Structure per Day:
  • First Hour: Learn new concepts/theory
  • Second Hour: Implement concepts and solve practice problems

Resources Recommended

1. Books:

   • “Introduction to Algorithms” by Cormen, Leiserson, Rivest, and Stein
   • “Data Structures and Algorithms Made Easy” by Narasimha Karumanchi

2. Online Platforms:

   • LeetCode (https://leetcode.com/)
   • HackerRank (https://www.hackerrank.com/)
   • GeeksforGeeks (https://www.geeksforgeeks.org/)

3. Courses:

   • Coursera: “Algorithms Specialization” by Stanford University
   • Udemy: “Data Structures and Algorithms: Deep Dive Using Java” by Tim Buchalka

4. Additional Tools:

   • Visualgo (https://visualgo.net/en) for visualizing data structures and algorithms

Week 1: Arrays and Strings

Day 1: Introduction to Arrays

• Theory:
  • What are arrays?
  • Array operations (insertion, deletion, traversal)
  • Fixed vs. dynamic arrays
• Practice:
  • Implement basic array operations in your preferred language
  • Solve simple array problems on LeetCode (e.g., Two Sum)

Day 2: Advanced Array Manipulations

• Theory:
  • Multi-dimensional arrays
  • Array sorting and searching algorithms
• Practice:
  • Implement sorting algorithms (Bubble, Selection, Insertion)
  • Practice searching problems (binary search)

Day 3: Strings Basics

• Theory:
  • String manipulation and common operations
  • Immutable vs. mutable strings
• Practice:
  • Implement string reversal, palindrome check
  • Solve string problems on LeetCode (e.g., Reverse String)

Day 4: Advanced String Algorithms

• Theory:
  • Pattern matching algorithms (KMP, Rabin-Karp)
  • Trie data structure introduction
• Practice:
  • Implement a basic pattern matching algorithm
  • Practice relevant problems

Day 5: Array and String Review

• Review:
  • Revisit challenging problems from the week
  • Implement a mini-project (e.g., basic text editor functionalities)
• Practice:
  • Mixed array and string problems on HackerRank or LeetCode

Week 2: Linked Lists

Day 6: Introduction to Linked Lists

• Theory:
  • Singly linked lists: structure, advantages, and disadvantages
• Practice:
  • Implement a singly linked list (insertion, deletion, traversal)
  • Solve basic linked list problems

Day 7: Doubly Linked Lists

• Theory:
  • Doubly linked lists: structure and operations
• Practice:
  • Implement a doubly linked list
  • Practice related problems

Day 8: Circular Linked Lists

• Theory:
  • Circular linked lists: structure and use-cases
• Practice:
  • Implement a circular linked list
  • Solve circular linked list problems

Day 9: Linked List Algorithms

• Theory:
  • Common algorithms: reversing a linked list, detecting cycles
• Practice:
  • Implement algorithms like reversing a list, Floyd’s cycle detection
  • Practice relevant problems

Day 10: Linked List Review and Project

• Review:
  • Revisit difficult linked list problems
• Project:
  • Implement a basic LRU Cache using linked lists and hashing
• Practice:
  • Mixed linked list problems

Week 3: Stacks and Queues

Day 11: Stacks Basics

• Theory:
  • Stack structure and operations (push, pop, peek)
  • Applications of stacks
• Practice:
  • Implement a stack using arrays and linked lists
  • Solve basic stack problems (e.g., Valid Parentheses)

Day 12: Advanced Stack Applications

• Theory:
  • Infix, prefix, and postfix expressions
  • Stack-based algorithms (e.g., expression evaluation)
• Practice:
  • Implement expression conversion and evaluation
  • Practice related problems

Day 13: Queues Basics

• Theory:
  • Queue structure and operations (enqueue, dequeue, front)
  • Types of queues: circular, priority, deque
• Practice:
  • Implement a queue using arrays and linked lists
  • Solve basic queue problems

Day 14: Advanced Queue Structures

• Theory:
  • Priority queues and heaps
  • Double-ended queues (deque)
• Practice:
  • Implement a priority queue using a heap
  • Practice related problems

Day 15: Stacks and Queues Review

• Review:
  • Revisit challenging stack and queue problems
• Project:
  • Implement a basic browser history using stacks
• Practice:
  • Mixed stack and queue problems

Week 4: Trees

Day 16: Introduction to Trees

• Theory:
  • Tree terminology (nodes, edges, height, depth)
  • Binary trees vs. n-ary trees
• Practice:
  • Implement a basic binary tree
  • Traverse a binary tree (in-order, pre-order, post-order)

Day 17: Binary Search Trees (BST)

• Theory:
  • Properties of BST
  • BST operations (insertion, deletion, search)
• Practice:
  • Implement BST operations
  • Solve BST-related problems

Day 18: Tree Traversals and Applications

• Theory:
  • Level-order traversal (BFS)
  • Applications of tree traversals
• Practice:
  • Implement BFS for trees
  • Practice traversal-related problems

Day 19: Balanced Trees

• Theory:
  • AVL Trees and Red-Black Trees
  • Importance of tree balancing
• Practice:
  • Implement AVL tree rotations
  • Solve problems requiring balanced trees

Day 20: Trees Review and Project

• Review:
  • Revisit difficult tree problems
• Project:
  • Implement a basic expression tree
• Practice:
  • Mixed tree problems on LeetCode or HackerRank

Week 5: Graphs

Day 21: Introduction to Graphs

• Theory:
  • Graph terminology (vertices, edges, directed, undirected)
  • Graph representations (adjacency list, adjacency matrix)
• Practice:
  • Implement graph representations
  • Basic graph traversal setup

Day 22: Depth-First Search (DFS)

• Theory:
  • DFS algorithm and applications
• Practice:
  • Implement DFS (recursive and iterative)
  • Solve DFS-related problems

Day 23: Breadth-First Search (BFS)

• Theory:
  • BFS algorithm and applications
• Practice:
  • Implement BFS
  • Solve BFS-related problems

Day 24: Shortest Path Algorithms

• Theory:
  • Dijkstra’s algorithm
  • Bellman-Ford algorithm
• Practice:
  • Implement Dijkstra’s algorithm
  • Solve shortest path problems

Day 25: Graph Review and Project

• Review:
  • Revisit challenging graph problems
• Project:
  • Implement a basic social network graph (friendships, connections)
• Practice:
  • Mixed graph problems

Week 6: Sorting and Searching Algorithms

Day 26: Basic Sorting Algorithms

• Theory:
  • Quick Sort, Merge Sort
• Practice:
  • Implement Quick Sort and Merge Sort
  • Solve sorting-related problems

Day 27: Advanced Sorting Algorithms

• Theory:
  • Heap Sort, Counting Sort, Radix Sort
• Practice:
  • Implement Heap Sort
  • Solve related problems

Day 28: Searching Algorithms

• Theory:
  • Binary Search and its applications
  • Interpolation and exponential search
• Practice:
  • Implement binary search
  • Solve searching-related problems

Day 29: Sorting and Searching Optimization

• Theory:
  • Time and space complexity analysis
  • Optimizing sorting and searching algorithms
• Practice:
  • Analyze and optimize existing implementations
  • Practice optimization problems

Day 30: Sorting and Searching Review

• Review:
  • Revisit difficult sorting and searching problems
• Project:
  • Implement a sorting visualizer using one or more sorting algorithms
• Practice:
  • Mixed sorting and searching problems

Week 7: Dynamic Programming and Greedy Algorithms

Day 31: Introduction to Dynamic Programming (DP)

• Theory:
  • Principles of DP: overlapping subproblems and optimal substructure
  • Memoization vs. Tabulation
• Practice:
  • Implement basic DP problems (e.g., Fibonacci)
  • Solve simple DP problems

Day 32: DP on Sequences

• Theory:
  • Longest Common Subsequence (LCS)
  • Longest Increasing Subsequence (LIS)
• Practice:
  • Implement LCS and LIS
  • Solve related problems

Day 33: DP on Trees and Graphs

• Theory:
  • DP techniques for tree and graph problems
• Practice:
  • Implement DP solutions on trees/graphs
  • Solve relevant problems

Day 34: Greedy Algorithms Basics

• Theory:
  • Principles of Greedy algorithms
  • When to use Greedy vs. DP
• Practice:
  • Implement Greedy algorithms (e.g., activity selection)
  • Solve Greedy-related problems

Day 35: Greedy Algorithms Advanced and Review

• Theory:
  • Advanced Greedy algorithms (e.g., Huffman Coding)
• Practice:
  • Implement advanced Greedy algorithms
  • Review DP and Greedy problems

Week 8: Advanced Topics and Review

Day 36: Heaps and Priority Queues

• Theory:
  • Heap data structure (min-heap, max-heap)
  • Priority queue implementation
• Practice:
  • Implement a heap
  • Solve heap-related problems

Day 37: Hashing and Hash Tables

• Theory:
  • Hash functions, collision resolution techniques
  • Implementing hash tables
• Practice:
  • Implement a hash table
  • Solve hashing-related problems

Day 38: Advanced Data Structures

• Theory:
  • Segment Trees, Fenwick Trees (Binary Indexed Trees)
  • Trie and Suffix Trees
• Practice:
  • Implement a Segment Tree or Trie
  • Solve related problems

Day 39: Backtracking and Recursion

• Theory:
  • Backtracking principles
  • Common backtracking problems (e.g., N-Queens)
• Practice:
  • Implement backtracking solutions
  • Solve backtracking-related problems

Day 40: Comprehensive Review and Mock Interviews

• Review:
  • Go through all topics covered
  • Revisit and solve previously challenging problems
• Mock Interviews:
  • Simulate coding interviews by solving a set of mixed DSA problems under time constraints
• Reflection:
  • Identify areas needing further practice

Post 8-Week Plan: Continued Practice and Advanced Learning

1. Regular Problem Solving:

• Platforms: Continue solving problems on LeetCode, HackerRank, and GeeksforGeeks.
• Consistency: Aim for at least 5 problems per week, varying in difficulty.

2. Participate in Coding Contests:

• Platforms: LeetCode Weekly Contests, HackerRank Contests, Codeforces.
• Benefits: Improve speed, problem-solving under pressure, exposure to diverse problems.

3. Advanced Topics (Optional):

• Topics:
  • Graph Algorithms (e.g., Floyd-Warshall, Kruskal’s, Prim’s)
  • Advanced DP techniques
  • Computational Geometry
  • Number Theory and Bit Manipulation
• Resources: Advanced sections in recommended books or specialized courses.

4. Projects:

• Examples:
  • Implement a personal library of data structures and algorithms.
  • Build applications that utilize various data structures (e.g., a task scheduler using heaps and priority queues).

5. Interview Preparation:

• Resources: “Cracking the Coding Interview” by Gayle Laakmann McDowell.
• Activities:
  • Mock interviews with peers or using platforms like Pramp.
  • Time-bound problem-solving sessions.

Tips for Success

1. Consistency is Key: Stick to the schedule diligently. Regular practice reinforces learning.
2. Understand, Don’t Memorize: Focus on understanding the underlying principles of each data structure and algorithm.
3. Implement from Scratch: Coding the data structures and algorithms yourself deepens comprehension.
4. Analyze Complexity: Always consider the time and space complexity of your solutions.
5. Seek Help When Stuck: Utilize forums like Stack Overflow or study groups to overcome challenges.
6. Reflect on Mistakes: Review and understand errors to avoid repeating them.
7. Balance Theory and Practice: Ensure you’re not just solving problems but also understanding why solutions work.

Embarking on this 8-week journey will significantly enhance your DSA skills, making you proficient in solving complex programming problems and preparing you for technical interviews or advanced software development challenges. Good luck!