How to Learn DSA in Python (2026 Guide): Complete Roadmap, Top Interview Questions & 3-Month Plan

How to Learn DSA in Python (2026) – Roadmap & 3-Month Plan

If you know Python but still struggle in technical interviews, the problem is usually not coding – it is Data Structures and Algorithms (DSA). Most companies like Google, Amazon, Microsoft, TCS, Infosys, Wipro, and Accenture test your problem-solving ability and optimization skills, not just syntax knowledge. In this complete 2026 guide on how to learn DSA in Python, you will learn:

  • Top DSA interview questions
  • Why DSA is important for placements
  • Step-by-step DSA roadmap in Python
  • Structured 3-month preparation plan
  • Best platforms to practice
  • Common mistakes to avoid

How to Learn DSA in Python?

To learn DSA in Python:

  1. Master Python basics
  2. Understand time & space complexity
  3. Practice arrays and strings
  4. Learn recursion and linked lists
  5. Study stacks, queues, trees, and heaps
  6. Master hashing and graphs
  7. Practice dynamic programming
  8. Solve 2–3 coding problems daily for 3 months

Consistency + structured roadmap = interview success.

Why Is DSA Important for IT Jobs?

Companies test DSA because it measures:

Example: Optimization Matters

Slow approach (O(n)):

def search(arr, x):
    for i in arr:
        if i == x:
            return True
    return False

Optimized approach (O(1) average):

def search(arr, x):
    s = set(arr)
    return x in s

Understanding this difference is what clears interviews.

Without DSA:

  • You fail coding rounds
  • You struggle in product-based companies
  • Career growth becomes slow

 

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Top 20 DSA Questions Asked in Interviews

These are commonly asked in product and service-based companies:

  • Reverse an array
  • Find second largest element
  • Palindrome check
  • Two Sum
  • Merge two sorted arrays
  • Missing number
  • Reverse linked list
  • Detect loop in linked list
  • Balanced parentheses
  • Binary search
  • Inorder traversal
  • Height of binary tree
  • Largest subarray with sum K
  • Kadane’s Algorithm
  • Dijkstra’s Algorithm
  • Longest Common Subsequence
  • Implement stack
  • Implement queue
  • BFS & DFS
  • Coin change problem

Complete DSA Roadmap in Python (Step-by-Step)

Follow this exact order.

Step 1: Python Fundamentals

Before DSA, you must know:

  • Variables & data types
  • Loops & conditions
  • Functions
  • Lists, sets, dictionaries
  • Basic OOP

Example:

student = {"name": "Ravi", "marks": 85}
print(student["name"])

Strong basics make DSA easier.

Step 2: Time and Space Complexity

Learn:

  • Big-O notation
  • Best, worst, average case
  • Time complexity
  • Space complexity

Example:

for i in range(n):
    print(i)

Time Complexity: O(n)

This helps compare solutions.

Step 3: Arrays and Strings

Most interviews focus heavily here.

Arrays

  • Traversal
  • Sorting
  • Searching
  • Two-pointer technique
  • Sliding window

Strings

  • Palindrome
  • Anagram
  • Substring problems

Step 4: Recursion and Backtracking

Used in:

  • Permutations
  • Subsets
  • Maze problems
  • N-Queens

Example:

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n - 1)

Step 5: Linked Lists

  • Singly linked list
  • Doubly linked list
  • Detect loop
  • Reverse list

Step 6: Stack and Queue

Stack → LIFO

Queue → FIFO

Used in:

  • Expression evaluation
  • Scheduling
  • Undo/Redo systems

Step 7: Trees and Binary Search Trees

Important topics:

  • Traversals (Inorder, Preorder, Postorder)
  • Height
  • Diameter
  • BST properties

Step 8: Heaps and Priority Queue

Used in:

  • Scheduling
  • Dijkstra
  • Top K elements

Step 9: Hashing

Hashing improves performance drastically.

Used in:

  • Two Sum
  • Frequency counting
  • Duplicate detection

Step 10: Graphs

Very important for product companies.

Learn:

  • BFS
  • DFS
  • Dijkstra
  • Topological sort

Step 11: Dynamic Programming (Very Important)

Most difficult but high scoring.

Topics:

  • Memoization
  • Tabulation
  • Knapsack
  • LCS
  • Coin Change

DP is frequently asked in Amazon, Microsoft, and Google interviews.

3-Month DSA Plan in Python

MonthFocus AreaGoal
Month 1Basics + Arrays + RecursionBuild foundation
Month 2Linked List + Trees + HashingIntermediate mastery
Month 3Graphs + DP + Mock InterviewsAdvanced problem solving

Practice daily.

 

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Best Platforms to Practice DSA

  • LeetCode
  • HackerRank
  • CodeChef
  • GeeksforGeeks

Solve:

  • 60 Easy
  • 80 Medium
  • 20 Hard

This level is enough for most companies.

Who Should Follow This Roadmap?

  • B.Tech students
  • Final year placement students
  • Freshers
  • Career switchers
  • Anyone preparing for coding interviews

Service vs Product-Based Company Focus

Company TypeDifficulty LevelFocus Areas
Service-basedEasy–MediumArrays, Strings, Basics
Mid-level ProductMediumTrees, Hashing, Graphs
Top ProductMedium–HardDP, Graphs, Advanced Trees

Common Mistakes to Avoid

  • Skipping basics
  • Memorizing solutions
  • Ignoring complexity
  • Solving randomly without roadmap
  • Not revising

Avoiding these saves months of effort.

Final Conclusion

Learning how to learn DSA in Python is not about memorizing solutions. It is about:

  • Understanding logic
  • Writing optimized code
  • Practicing consistently
  • Following a structured roadmap

If you seriously follow this 3-month DSA plan in Python and solve problems daily, you can confidently clear most coding interviews in service-based and mid-level product companies.

Start today. Stay consistent. Build your problem-solving mindset.

Your IT career depends on it.

 

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Frequently Asked Questions

Yes, if you practice consistently 2–3 problems daily and revise weekly.

Basic Python knowledge is required, but you don’t need advanced programming.

Python is beginner-friendly. C++ offers better memory control. Both are accepted in interviews.

2–3 high-quality problems daily is enough for steady improvement.

Author

Embedded Systems trainer – IIES

Updated On: 11-02-26


10+ years of hands-on experience delivering practical training in Embedded Systems and it's design