Finding Your Starting Point:

Finding a solid way to start programming can feel overwhelming. You get lost in definitions, frameworks, and tutorials — and it’s hard to know where to begin.

But if we strip everything down to the most important foundation, one concept stands above all others: the algorithm.

What Is an Algorithm?

An algorithm is simply a set of steps used to solve a problem or create something. It’s the sequence of actions you take to reach a result.

You use algorithms every day — even outside coding.

Making coffee: boil water → add coffee → pour → stir Sorting your books: compare titles → arrange alphabetically

That’s all an algorithm is — a structured way to reach a goal.

What Makes a “Good” Algorithm?

_Not all algorithms are equal. _Some take more time or use more memory than others.

A good algorithm is one that:

Solves the problem correctly Uses the least possible time Uses the least possible space

So how can we measure that? That’s where Big O Notation comes in.

Big O Notation — Measuring Efficiency

Big O tells us how the performance of an algorithm changes as the input grows. It focuses on the worst-case scenario: how long (or how much memory) your code might take for n elements.

Here are the most common types:

O(1) => Constant => Always takes the same time, no matter the input size. (Best case!)

O(log n) => Logarithmic =>Still very efficient — time increases slowly as input grows.

O(n) => Linear => Time grows in proportion to input size.

O(n²) => Quadratic => Much slower — time grows dramatically as input increases.

Think of it like this:

Searching in a sorted list (binary search) → O(log n) Looping through every item once → O(n) Double nested loops → O(n²)

Algorithms and Data Structures:

Algorithms work on data — but the way we store and organize that data matters too. That’s where data structures come in.

Different data structures (arrays, stacks, queues, linked lists, trees, etc.) are optimized for different operations. For example:

Searching through an array might take O(n) Searching through a hash table can be O(1)

Choosing the right structure can dramatically change performance.

What’s Next?

In this series, we’ll explore: The most important data structures Their time and space complexities (Big O) Real LeetCode problems related to each topic Pseudocode examples to understand how they work

By the end, you’ll not only know what each structure does — you’ll know when and why to use it.