Computer Science Subject Expert
Specialized knowledge for computer science studying, problem-solving, and note creation.
Topic Coverage
mindmap
root((Computer Science))
Algorithms
Sorting
Searching
Graph Algorithms
Dynamic Programming
Data Structures
Arrays & Lists
Trees & Graphs
Hash Tables
Heaps & Queues
Systems
Operating Systems
Networks
Databases
Theory
Complexity
Automata
Computability
Quick Reference Links
Diagram Patterns
Algorithm Flowchart
flowchart TB
A[Start] --> B{Condition?}
B -->|Yes| C[Process A]
B -->|No| D[Process B]
C --> E[End]
D --> E
Data Structure Visualization
graph TB
subgraph "Binary Search Tree"
A((8)) --> B((3))
A --> C((10))
B --> D((1))
B --> E((6))
C --> F((14))
end
Sequence Diagram (API/Process)
sequenceDiagram
Client->>Server: Request
Server->>Database: Query
Database-->>Server: Results
Server-->>Client: Response
Problem-Solving Framework
Algorithm Design Steps
- Understand - Clarify inputs, outputs, constraints
- Examples - Work through 2-3 examples by hand
- Brute Force - Start with the obvious O(n²) or O(n!) solution
- Optimize - Apply patterns (two pointers, sliding window, etc.)
- Implement - Write clean, modular code
- Test - Edge cases (empty, single element, duplicates)
Common Patterns
| Pattern | Use When | Example |
|---|
| Two Pointers | Sorted array, find pairs | Two Sum (sorted) |
| Sliding Window | Contiguous subarray | Max sum subarray |
| Hash Map | O(1) lookups needed | Two Sum (unsorted) |
| BFS | Shortest path (unweighted) | Maze solving |
| DFS | Explore all paths | Permutations |
| Binary Search | Sorted data, find element | Search rotated array |
| Dynamic Programming | Overlapping subproblems | Fibonacci, Knapsack |
Code Template Patterns
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 # Not found
BFS Template
from collections import deque
def bfs(start, target):
queue = deque([start])
visited = {start}
while queue:
node = queue.popleft()
if node == target:
return True
for neighbor in get_neighbors(node):
if neighbor not in visited:
visited.add(neighbor)
queue.append(neighbor)
return False
DFS Template
def dfs(node, visited):
if node in visited:
return
visited.add(node)
# Process node
for neighbor in get_neighbors(node):
dfs(neighbor, visited)
Complexity Quick Reference
| Operation | Array | Linked List | Hash Table | BST (avg) |
|---|
| Access | O(1) | O(n) | O(1) | O(log n) |
| Search | O(n) | O(n) | O(1) | O(log n) |
| Insert | O(n) | O(1) | O(1) | O(log n) |
| Delete | O(n) | O(1) | O(1) | O(log n) |