, Complete Data Structures & Algorithms Notes
Version 1: Interview-Focused Comprehensive Guide
TABLE OF CONTENTS
PART 1: FUNDAMENTALS
1. Time & Space Complexity Analysis
2. Big O, Omega, Theta Notation
3. Asymptotic Analysis
PART 2: DATA STRUCTURES
4. Arrays & Strings
5. Linked Lists
6. Stacks & Queues
7. Trees & Binary Trees
8. Binary Search Trees
9. Heaps & Priority Queues
10. Hash Tables & Hash Maps
11. Graphs
12. Tries
PART 3: ALGORITHMS
13. Searching Algorithms
14. Sorting Algorithms
15. Recursion & Backtracking
16. Dynamic Programming
17. Greedy Algorithms
18. Divide & Conquer
19. Graph Algorithms
20. String Algorithms
PART 4: PROBLEM SOLVING
21. Problem-Solving Patterns
22. Top 50 Interview Questions
23. Company-Wise Questions
PART 1: FUNDAMENTALS
Chapter 1: Time & Space Complexity Analysis
Page 1
, Complete Data Structures & Algorithms Notes
What is Complexity?
Complexity measures how an algorithm's resource usage (time/memory) grows as input size increases.
Why Important?
- Compare algorithm efficiency
- Predict performance on large datasets
- Optimize code for interviews
Time Com
Measures number of operations as function of input size n.
Space Complexity:
Measures memory usage as function of input size n.
Example 1: Linear Search
def linear_search(arr, target):
for i in range(len(arr)): # runs n times
if arr[i] == target:
return i
return -1
# Time: O(n) - worst case checks all elements
# Space: O(1) - only uses few variables
Example 2: Binary Search
def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1
# Time: O(log n) - halves search space each iteration
# Space: O(1) - only uses few variables
Chapter 2: Big O, Omega, Theta Notation
Big O (O) - Upper Bound
Worst-case scenario. Algorithm will never be slower than this.
Common Big O Complexities (Best to Worst):
1. **O(1) - Constant Time** BEST
- - Accessing array element
- - Hash table lookup
- - Push/Pop from stack
Exampl
Page 2
Version 1: Interview-Focused Comprehensive Guide
TABLE OF CONTENTS
PART 1: FUNDAMENTALS
1. Time & Space Complexity Analysis
2. Big O, Omega, Theta Notation
3. Asymptotic Analysis
PART 2: DATA STRUCTURES
4. Arrays & Strings
5. Linked Lists
6. Stacks & Queues
7. Trees & Binary Trees
8. Binary Search Trees
9. Heaps & Priority Queues
10. Hash Tables & Hash Maps
11. Graphs
12. Tries
PART 3: ALGORITHMS
13. Searching Algorithms
14. Sorting Algorithms
15. Recursion & Backtracking
16. Dynamic Programming
17. Greedy Algorithms
18. Divide & Conquer
19. Graph Algorithms
20. String Algorithms
PART 4: PROBLEM SOLVING
21. Problem-Solving Patterns
22. Top 50 Interview Questions
23. Company-Wise Questions
PART 1: FUNDAMENTALS
Chapter 1: Time & Space Complexity Analysis
Page 1
, Complete Data Structures & Algorithms Notes
What is Complexity?
Complexity measures how an algorithm's resource usage (time/memory) grows as input size increases.
Why Important?
- Compare algorithm efficiency
- Predict performance on large datasets
- Optimize code for interviews
Time Com
Measures number of operations as function of input size n.
Space Complexity:
Measures memory usage as function of input size n.
Example 1: Linear Search
def linear_search(arr, target):
for i in range(len(arr)): # runs n times
if arr[i] == target:
return i
return -1
# Time: O(n) - worst case checks all elements
# Space: O(1) - only uses few variables
Example 2: Binary Search
def binary_search(arr, target):
left, right = 0, len(arr) - 1
while left <= right:
mid = (left + right) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1
# Time: O(log n) - halves search space each iteration
# Space: O(1) - only uses few variables
Chapter 2: Big O, Omega, Theta Notation
Big O (O) - Upper Bound
Worst-case scenario. Algorithm will never be slower than this.
Common Big O Complexities (Best to Worst):
1. **O(1) - Constant Time** BEST
- - Accessing array element
- - Hash table lookup
- - Push/Pop from stack
Exampl
Page 2
