roadmap.txt

Master the Basics:

Understand the fundamentals of data types (list, set, dict, tuple) and their time complexities.
Learn how to implement basic data structures manually, like stacks, queues, and linked lists.
Focus on Problem-Solving:

Start with simple problems to build confidence and gradually move to complex ones.
Platforms like LeetCode, HackerRank, and GeeksforGeeks are great for practice.
Analyze Time and Space Complexity:

Practice calculating the Big-O notation for your solutions.
Compare different approaches for the same problem.
Understand Recursion and Iteration:

Many DSA problems require a good understanding of recursion (e.g., divide-and-conquer algorithms).
Learn to convert recursive solutions to iterative ones when necessary.
Learn Pythonic Approaches:

Use list comprehensions, generator expressions, and built-in functions (map, filter, zip) when appropriate.
Explore modules like collections for deque, Counter, and defaultdict.
Core Chapters to Cover:
Arrays and Strings:

Sliding window, two-pointer techniques.
Common problems: Subarray sum, longest substring without repeating characters.
Linked Lists:

Types: Singly, doubly, circular.
Problems: Reversing a list, detecting loops, merging two sorted lists.
Stacks and Queues:

Implement using arrays or collections.deque.
Problems: Valid parentheses, next greater element.
Hashing:

Learn about hash tables and hash maps (dictionaries in Python).
Problems: Two-sum, longest consecutive sequence.
Recursion and Backtracking:

Basics of recursion, memoization, and dynamic programming.
Problems: N-Queens, subsets, permutations.
Trees and Graphs:

Binary trees, binary search trees, traversal algorithms (BFS, DFS).
Graph representations (adjacency list, adjacency matrix).
Problems: Shortest path, connected components.
Sorting and Searching:

Sorting algorithms: Merge sort, quick sort.
Searching techniques: Binary search and variations.
Dynamic Programming:

Focus on problems like knapsack, longest common subsequence, and coin change.
Greedy Algorithms:

Problems: Activity selection, Huffman encoding.
Heap and Priority Queue:

Use the heapq library for min-heaps.
Problems: Kth largest/smallest element, merge K sorted arrays.
Recommended Practice Platforms:
LeetCode: For structured DSA practice.
GeeksforGeeks: For topic-wise explanations and problems.
Codeforces/CodeChef: For competitive programming challenges.
HackerRank: For beginner-friendly problems.
AlgoExpert: For curated interview problems (paid).


Resources to Learn DSA:
Books:
Introduction to Algorithms by Cormen (CLRS) (for theory).
Grokking Algorithms by Aditya Bhargava (for beginner-friendly visuals).
YouTube Channels:
NeetCode, Tech with Tim, Apna College.
Courses:
Data Structures and Algorithms Specialization by Coursera.
Master the Coding Interview: Big Tech (Udemy).
Plan for Practice:
Dedicate time daily to solve problems (start with 2–3 simple problems and scale up).
Maintain a notebook or a document to record solutions and approaches.
Reflect on problems you couldn’t solve; analyze the optimal solution.