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

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
| Month | Focus Area | Goal |
|---|
| Month 1 | Basics + Arrays + Recursion | Build foundation |
| Month 2 | Linked List + Trees + Hashing | Intermediate mastery |
| Month 3 | Graphs + DP + Mock Interviews | Advanced problem solving |
Practice daily.

Best Platforms to Practice DSA
- LeetCode
- HackerRank
- CodeChef
- GeeksforGeeks
Solve:
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 Type | Difficulty Level | Focus Areas |
|---|
| Service-based | Easy–Medium | Arrays, Strings, Basics |
| Mid-level Product | Medium | Trees, Hashing, Graphs |
| Top Product | Medium–Hard | DP, 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.
