Complete DSA Roadmap for Product-Based Companies

This roadmap is designed to guide you through your journey of learning Data Structures and Algorithms (DSA) in a structured, phase-wise manner. Starting from the basics, we’ll cover fundamental concepts, algorithms, and advanced problem-solving techniques, with a clear focus on understanding and practice.

📅 Phase-wise Learning Plan

📘 PHASE 0: Preparation & Mindset (Day 0)

🔹 Before You Start

• Choose Your Language: Start with a beginner-friendly language like Python. If you're comfortable with another language (Java, C++), feel free to use that.
• Learn the Basic Syntax: Get familiar with the basic syntax of your chosen programming language (e.g., loops, conditions, functions, classes, etc.).
• Setup Your Coding Environment:
  • Install your preferred IDE (VS Code, PyCharm, etc.)
  • Set up accounts on online platforms for practice:
  • LeetCode
  • GeeksforGeeks
  • HackerRank
  • Codeforces

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📘 PHASE 1: Foundations (Day 1–5)

🔹 What is DSA?

• Data Structures: Learn how data is organized (e.g., Arrays, Linked Lists, Trees, etc.)
• Algorithms: Understand how data is manipulated (e.g., Searching, Sorting).

🔹 Time & Space Complexity

• Time Complexity: Understand Big-O notation and how to analyze the time complexity of algorithms.
• Space Complexity: Learn how to analyze the memory usage of algorithms.

🔹 Setup Your Environment

• Install an IDE or Text Editor (VS Code, PyCharm, etc.)
• Set up accounts on LeetCode, GeeksforGeeks, HackerRank, Codeforces for practice.
• Choose your preferred language (Python, Java, C++) and get comfortable with basic syntax.

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📘 PHASE 2: Arrays & Strings (Day 6–10)

🔹 Arrays

• Definition: A collection of elements stored in contiguous memory locations. They are indexed by integers.
• Why Use:
  • Arrays are great for storing data when you need fast access by index.
  • When you know the size in advance, arrays are ideal.

• Operations:

  • Access: arr[i]
  • Insertion: Adding elements to specific positions.
  • Deletion: Removing elements from a specific position.
  • Traversal: Iterating over each element.
  • Searching: Linear Search, Binary Search (for sorted arrays).

• Common Problems:

  • Reverse an array.
  • Find the maximum and minimum elements.
  • Rotate an array.
  • Subarray with a given sum.
  • Find the Kth largest/smallest element.

🔹 Strings

• Definition: A sequence of characters.
• Why Use:
  • Used for text processing, searching, and pattern matching.

• Operations:

  • Substring extraction.
  • Concatenation: Joining strings.
  • Pattern matching: Searching for specific patterns (e.g., finding substrings).

• Common Problems:

  • Palindrome check.
  • Anagram check.
  • Longest Common Substring/Subsequence.
  • Count and say problem.

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📘 PHASE 3: Linked Lists & Stacks (Day 11–15)

🔹 Linked Lists

• Definition: A linear collection of nodes where each node contains data and a pointer to the next node.
• Why Use:
  • Useful for dynamic memory allocation (no need to define the size in advance).
  • When insertion/deletion at arbitrary positions is more frequent than array operations.

• Operations:

  • Insertion: Insert at head, tail, or specific position.
  • Deletion: Remove nodes.
  • Traversal: Visit each node.
  • Reversal: Reverse the list.

• Common Problems:

  • Reverse a linked list.
  • Find the middle element.
  • Detect cycle (Floyd’s Tortoise and Hare algorithm).
  • Merge two sorted lists.

🔹 Stacks

• Definition: A collection of elements that follows the LIFO (Last In, First Out) principle.
• Why Use:
  • Useful for problems where you need to remember the most recent element.
  • Example: Undo functionality in text editors, recursive calls, and parentheses matching.

• Operations:

  • Push (add element).
  • Pop (remove element).
  • Peek (view top element).

• Common Problems:

  • Valid Parentheses.
  • Next Greater Element.
  • Reverse a stack using recursion.
  • Evaluate postfix or prefix expressions.

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📘 PHASE 4: Queues & Hashing (Day 16–20)

🔹 Queues

• Definition: A collection of elements that follows the FIFO (First In, First Out) principle.
• Why Use:
  • When you need to process elements in the same order they arrive (e.g., in scheduling, queues in networking).

• Operations:

  • Enqueue: Add element to the rear.
  • Dequeue: Remove element from the front.
  • Front and Rear: Peek at front/rear elements.

• Common Problems:

  • Implement queue using stacks.
  • First non-repeating character.
  • Sliding window maximum.

🔹 Hashing (HashMap/HashSet)

• Definition: A data structure that maps keys to values using a hash function.
• Why Use:
  • To store and retrieve data in constant time on average.
  • Ideal for problems requiring fast lookups, counts, and uniqueness checks.

• Operations:

  • Insert: Add key-value pair.
  • Lookup: Retrieve value using a key.
  • Delete: Remove key-value pair.

• Common Problems:

  • Two Sum problem (find two numbers that add up to a target).
  • Longest Consecutive Sequence.
  • Group Anagrams.
  • First non-repeating character in a string.

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📘 PHASE 5: Recursion & Backtracking (Day 21–25)

🔹 Recursion

• Definition: A technique where a function calls itself to solve smaller instances of the problem.

• Why Use:

  • Useful for problems that can be broken down into smaller, similar subproblems.
  • Common in problems like tree traversal, factorial calculation, Fibonacci series, etc.

• Common Problems:

  • Factorial.
  • Fibonacci series.
  • Tower of Hanoi.
  • N-Queens problem.
  • Subset generation (Power Set).

🔹 Backtracking

• Definition: A method for finding all (or some) solutions by exploring all possible options and backtracking when one path doesn’t work.

• Why Use:

  • Useful when you need to explore all potential solutions or configurations (e.g., permutations, combinations).
  • Example: Solving puzzles, finding valid solutions in constraint-based problems.

• Common Problems:

  • Permutations and combinations.
  • Sudoku Solver.
  • Subset Sum.
  • Word search in a grid (backtrack to find words).

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📘 PHASE 6: Trees (Day 26–30)

🔹 Binary Trees

• Definition: A tree in which each node has at most two children (left and right).

• Why Use:

  • Trees are used for hierarchical data representation, such as file systems or organizational charts.
  • Binary trees are ideal when you need efficient searching and sorting (e.g., Binary Search Tree).

• Common Problems:

  • Find the height of the tree.
  • Diameter of a Binary Tree.
  • Symmetric Tree.

🔹 Binary Search Tree (BST)

• Definition: A binary tree where each node follows the rule: left subtree nodes are smaller and right subtree nodes are larger.

• Why Use:

  • Efficient searching, insertion, and deletion operations.

• Common Problems:

  • Lowest Common Ancestor (LCA).
  • Convert sorted array to a BST.
  • Balanced tree check.

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📘 PHASE 7: Heaps & Graphs (Day 31–35)

🔹 Heaps (Priority Queue)

• Definition: A complete binary tree that satisfies the heap property (min-heap or max-heap).

• Why Use:

  • Useful for efficiently retrieving the smallest/largest element.
  • Common in algorithms like Dijkstra’s shortest path and heap sort.

• Common Problems:

  • Kth largest element in an array.
  • Merge k sorted arrays.
  • Top K frequent elements.

🔹 Graphs

• Definition: A collection of nodes (vertices) connected by edges.

• Why Use:

  • Used to model complex networks, such as social networks, transportation systems, and communication networks.

• Common Problems:

  • Cycle Detection in a graph.
  • Shortest Path (Dijkstra’s and Bellman-Ford algorithms).
  • Topological Sort.

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📘 PHASE 8: Advanced Algorithms (Day 36–40)

🔹 Dynamic Programming (DP)

• Definition: Breaks a problem into smaller subproblems, stores results to avoid recomputation (Memoization, Tabulation).

• Why Use:

  • To solve optimization problems by breaking them down into simpler subproblems.
  • Common for problems with overlapping subproblems.

• Common Problems:

  • 0/1 Knapsack problem.
  • Longest Common Subsequence (LCS).
  • Coin Change problem.
  • DP on Grids (Matrix path sum).

🔹 Greedy Algorithms

• Definition: Makes the locally optimal choice at each step with the hope of finding the global optimum.

• Why Use:

  • Ideal for problems where making local optimal choices leads to a global optimum.
  • Common in scheduling problems, minimum spanning trees, and Huffman encoding.

• Common Problems:

  • Fractional Knapsack.
  • Activity Selection.
  • Huffman Encoding.

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📘 PHASE 9: Mock Interviews & Practice (Day 41–50)

🔹 Mock Interviews:

• Simulate interview scenarios using Pramp, Interviewing.io, or by solving LeetCode Premium problems under timed conditions.

🔹 Advanced Problem Solving:

• Practice Hard-level problems on platforms like LeetCode, Codeforces, and HackerRank.
• Focus on problems from multiple DSA categories to strengthen your problem-solving skills.

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📚 Final Tips:

• Daily Practice: Aim to solve 2–3 problems daily (mix of Easy, Medium, and Hard).
• Consistency: Practice at least 1 hour per day.
• Revise: Regularly revise past problems and concepts.
• Focus on Optimizing Solutions: As you solve problems, focus on improving the time and space complexity of your solutions.