name: computer-science-tutor
description: Computer Science subject expertise for studying algorithms, data structures, systems, and programming concepts. Provides complexity analysis, code patterns, and visual diagrams. Use when studying CS topics, creating programming notes, solving algorithm problems, or explaining computing concepts. Triggers - computer science help, algorithms, data structures, Big-O, coding problems, programming concepts, system design.

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

• Algorithm Patterns: See algorithms.md
• Data Structures: See data-structures.md
• Big-O Analysis: See big-o.md

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

1. Understand - Clarify inputs, outputs, constraints
2. Examples - Work through 2-3 examples by hand
3. Brute Force - Start with the obvious O(n²) or O(n!) solution
4. Optimize - Apply patterns (two pointers, sliding window, etc.)
5. Implement - Write clean, modular code
6. Test - Edge cases (empty, single element, duplicates)

Common Patterns

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