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Complexity & Foundations
Big O notation Big Omega notation Big Theta notation Constant time Logarithmic time Linear time Quadratic time Amortized analysis Space complexity Recursion stack
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Array traversal Array insertion Array deletion Two pointers Sliding window Prefix sums String reversal Palindrome check Frequency counting Matrix traversal
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Stacks & Queues
Stack push pop Balanced parentheses Monotonic stack Queue enqueue dequeue Circular queue Deque Priority queue concept Breadth-first queue Min stack Evaluate postfix expression
Hashing
Hash map lookup Hash set membership Two sum Group anagrams First unique character Collision concept Load factor concept Open addressing concept Separate chaining concept Memoization with hash map
Trees & Heaps
Binary tree traversal Preorder traversal Inorder traversal Postorder traversal Level order traversal Binary search tree search BST insertion Tree height Min heap Heap sort
Graphs
Adjacency list Adjacency matrix Depth-first search Breadth-first search Connected components Cycle detection directed graph Topological sort Dijkstra shortest path Union find Minimum spanning tree concept
Sorting Searching & DP
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Data Structures & Algorithms / Complexity & Foundations

Big O notation

Learn Big O notation in Data Structures & Algorithms with a focused explanation, a topic-specific example, edge guidance, and a mastery check.

12 min read • Beginner
data-structures-algorithms-001.py
python 
def contains(items,target):
    for item in items:          # O(n)
        if item==target: return True
    return False
print(contains([4,8,15,16],15))
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Big Omega notation