Introduction to Data Structures & Algorithms

What is DSA?

Data Structures and Algorithms (DSA) is the study of two intertwined ideas: how to organize data so it can be accessed efficiently, and how to operate on that data to solve problems.

Every program you write uses both — even simple scripts. A loop that searches a list is an algorithm. The list itself is a data structure. The question DSA asks is: can you do better?

The Core Problem

Two programs can produce identical output for the same input, but one finishes in a millisecond while the other runs for ten minutes. The difference is almost never the programming language or the hardware — it's the choice of algorithm and data structure.

Consider a phone book with 1,000,000 entries. You want to find "Smith, John":

• Linear scan — check every entry from page 1. In the worst case: 1,000,000 checks.
• Binary search — open to the middle, decide which half Smith is in, repeat. Worst case: ~20 checks.

Same result. 50,000× fewer operations. This is the value of algorithmic thinking.

What You Need to Know First

Before diving into individual structures and algorithms, this track covers two foundational concepts that everything else depends on:

1. Time Complexity — How an algorithm's running time grows with input size. This is what people mean when they say "Big-O."
2. Space Complexity — How much memory an algorithm uses as input grows.
3. Master Theorem — A formula for solving the recurrence relations that arise in divide-and-conquer algorithms.

These three topics are treated as separate articles because they deserve full attention. Many people skip them and learn algorithms by pattern-matching — then struggle when a problem doesn't fit a memorized template.

How to Use This Track

Each article in this track:

• Introduces one concept with clear intuition
• Works through examples from simple to complex
• Shows the tradeoffs, not just the happy path
• Ends with patterns to recognize in the wild

Read in order. Each article assumes the previous ones.

The Learning Mindset

Don't optimize for memorization. Interviewers don't care if you've seen the exact problem before. They care whether you can reason about it. Build intuition — the rest follows.

Implement, don't just read. Reading code teaches you syntax. Writing it from scratch teaches you structure and forces you to confront the details you glossed over.

Trace through small examples. Pick an input of size 4 or 5 and walk through every step by hand. This is how understanding becomes durable.

What's Covered in This Track

Start with the next article: Time Complexity.