Computer Science Basics for Beginners

⏱️ Time and Space Complexity (Intro)

When writing code, it’s not just about solving the problem — it’s about solving it efficiently. Time and space complexity help you evaluate how your code performs as the input grows. These concepts are essential for writing optimized code, especially in technical interviews.

🔍 What is Time Complexity?

Time complexity refers to how the time taken by an algorithm increases with input size. It’s measured in terms of Big O notation, which describes the worst-case scenario.

⏳ Common Time Complexities

🧠 What is Space Complexity?

Space complexity measures the amount of memory an algorithm uses relative to the input size. This includes variables, data structures, function call stack, etc.

For example, a program that stores all user records in a list will use more memory as the number of users grows — this is space complexity.

📌 Example Code: Time Complexity Comparison

# O(n): Linear time
def linear_search(arr, key):
    for i in arr:
        if i == key:
            return True
    return False

# O(1): Constant time
def get_first_item(arr):
    return arr[0]
  

📘 Why It Matters

• Helps you write scalable code
• Essential for cracking coding interviews
• Lets you choose the right data structure or algorithm

💡 Tips to Remember

• Focus on the worst-case time complexity
• Avoid nested loops when possible
• Use hash maps (dictionaries) for faster lookups
• Practice common algorithms (searching, sorting, recursion)

✅ Coming Next

In the next chapter, we’ll learn about Logic Gates & Bitwise Operators — the digital building blocks behind every computer.