Why You Need to Learn Complex Algorithms

An algorithm refers to a series of commands and procedures to solve a problem.

In simple terms, an algorithm is a step-by-step guide to solve a given problem.

So, why should we learn complex algorithms?

Algorithms significantly help train 'ways of thinking' for both IT professionals and the general public alike.

1. Improved Problem-Solving Skills: Instead of solving complex problems based on intuition, algorithmic thinking helps find the most efficient methods, enhancing problem-solving capacities.

2. Development of Logical Thinking: By understanding algorithms, you learn to break down complex issues into smaller steps. For example, you could design a step-by-step checklist to efficiently manage repetitive household chores.

3. Enhanced Decision-Making: By making the process of finding the most efficient method a habit, you can make better decisions.

Additionally, efficient algorithms can significantly reduce the operational costs of a company's IT infrastructure. For example, optimized algorithms can improve database search speed or increase server throughput.

Below is a Python algorithm that sorts an array of random numbers such as [64, 34, 25, 12, 22, 11, 90] in ascending order.

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def bubble_sort(arr):
    n = len(arr)
    for i in range(n):  # Repeat for the length of the array
        for j in range(0, n-i-1):  # Repeat for the length minus i
            if arr[j] > arr[j+1]:  # If the current element is greater than the next element
                arr[j], arr[j+1] = arr[j+1], arr[j]  # Swap their positions
    return arr


# Original array
example_array = [64, 34, 25, 12, 22, 11, 90]


# Perform sorting
sorted_array = bubble_sort(example_array)
print("Sorted array:", sorted_array)  # [11, 12, 22, 25, 34, 64, 90]

The bubble_sort() function used here compares adjacent elements, swapping their positions if the current element is greater than the next element.

This type of sorting method is known as 'bubble sort.'

Algorithms are used not only for sorting but also for solving various real-world problems such as search, finding the shortest path, and more.

In the next lesson, we will review 'complexity,' which evaluates the performance of an algorithm.