What Is Algorithms ?

Introduction

What is an Algorithm?

An algorithm is a series of steps to solve a problem or carry out a task. To develop an algorithm, start by using decomposition to break down the problem into smaller tasks, then look for patterns in these tasks, and finally ignore unimportant details. This should give you the information you need to create an algorithm made of small steps that can all be described very clearly.

Step by Step

Algorithms describe a series of steps that must happen in sequence in order for the problem to be solved. In athletics, the triple jump competition involves the competitor running, then performing a hop, a bounce, and a horizontal jump at specific places in order to record a successful effort.

Algorithms in Daily Life

Algorithms are not only made for computers. They are also part of our everyday life:

• Google Search: The algorithm decides which results appear at the top.

• Facebook / TikTok:  The algorithm determines what content you see in your feed.

• GPS Navigation: The algorithm shows you the shortest route to your destination.

• Online Shopping: The algorithm recommends products that match your interests.

Benefits of Understanding Algorithms

1. They help you solve problems more effectively.

2. They improve logical thinking and mathematical skills.

3. They open doors to future careers such as AI, Data Science, and Cybersecurity.

How to Learn Algorithms?

1. Educational Websites – GeeksforGeeks, LeetCode, HackerRank.

2. YouTube Tutorials – Clear and easy-to-follow video explanations.

3. Practice Coding – Write small programs to apply what you learn.

 

Types of Algorithms

Algorithms have many types that are used to solve different problems. The most common ones include:

1. Sorting Algorithms

These arrange data in an orderly manner.

• Bubble Sort – Compares two adjacent elements and swaps them if they are in the wrong order. It is simple but slow when the data set is large.

• Merge Sort – Divides the data into smaller parts and then merges them back in order. It is much faster for large data sets.

Real-life example: Arranging students’ names on a school list.

2. Searching Algorithms

These help in finding specific information within a large set of data.

• Linear Search – Checks each element one by one until it finds the target. Simple but slow if the data is large.

• Binary Search – Works on sorted data. It repeatedly divides the list in half until it finds the target. Much faster for big data.
  

  Real-life example: Looking up a person’s name in your phone contacts.

3. Graph Algorithms

Used when data is connected through relationships (nodes and edges).

• Breadth First Search (BFS) – Explores all immediate neighbors before moving farther.

• Depth First Search (DFS) – Explores one branch fully before backtracking.

• Dijkstra’s Algorithm – Finds the shortest path from one point to another.

Real-life example: GPS navigation showing the shortest route to your destination.

4. Machine Learning Algorithms

These are modern algorithms that power Artificial Intelligence (AI).

• Decision Trees – Make decisions by following different conditions step by step.

• Neural Networks – Mimic the human brain to learn complex patterns from data.

Real-life example: Face recognition (Face ID) and YouTube video recommendations.   

How to write an Algorithm?

1. Writing algorithm does not have any well-defined standards.

2. Algorithms are never created to support a specific programming language.

3. As we all know, basic code features such as loops (do, for, while all programming languages share), flow control (if-else), and so on.

4. We usually create algorithms step by step; however, this isn’t always the case.

5. After the problem domain has been well-defined, algorithm writing is a procedure that is carried out.

Characteristics of an Algorithm

1. Clear and Unambiguous

2. Well-defined Inputs

3. Well-defined Outputs

4. Finite-ness

5. Feasible

6. Language Independent

Algorithm Complexity

Two factors can be used to evaluate the algorithms performance:

1. Time Complexity: The amount of time required to finish an algorithm’s execution is known as its time complexity.

2. Space Complexity: The quantity of space required to solve a problem and produce an output is referred as space complexity.

 
Algorithms vs Programs

An algorithm can be compared to a map and a program to a city. While algorithms focus only on logic, programs contain language-specific details and syntax.

Algorithm Efficiency

Two different roads might lead to the same place, but taking the motorway is faster than a winding mountain path. This is true for algorithms as well. Two algorithms might produce the same result, but one may be more efficient than the other.

Algorithm qualities

There’s always a trade-off between speed, space efficiency, and simplicity. Picking the right algorithm depends on which factors are most important to the problem.

 

Conclusion: Algorithms are the foundation of modern technology. From simple daily tasks like sorting names to advanced systems like Artificial Intelligence, they guide how problems are solved efficiently and systematically. Understanding algorithms not only strengthens problem-solving and logical thinking but also opens doors to exciting careers in fields such as AI, Data Science, and Cybersecurity.