Learning Journey
Problem solving is the foundation of software engineering. It is easy to forget, among all the layers of abstraction we have built over the years, that the essence of programming is effective use of loops and conditionals. When I was first introduced to algorithms and data structures, I quickly developed a loathing for the topic. Why spend hours solving riddles instead of building something useful? I still believe today that practical skills and hands-on experience matter more than puzzle-solving prowess. But with time, I developed an intellectual curiosity about algorithms and decided to revisit the topic on my own terms.
Studying algorithms because I wanted to was a completely different experience. Instead of a rat race to become the best riddle-solver of them all, it became a slow-paced, thoughtful exploration. I encourage you to take a similar approach. The journey matters more than the destination.
The Problem with Grinding
There is a prevalent belief that the path to algorithmic mastery is through sheer volume: solve a thousand problems and you will be ready for anything. This approach has its merits, but it also has a fundamental flaw. Memorizing solutions to individual problems does not teach you how to think. You end up with a mental library of tricks that only work when you recognize the exact problem you have seen before.
A better approach is to learn patterns. Most algorithmic problems are variations on a small set of core techniques. Once you internalize these patterns, new problems stop feeling like puzzles you have never seen. Instead, they become familiar challenges dressed in different clothing. You learn to recognize the shape of a problem and reach for the right tool.
Why Start with Fundamentals?
Before diving into data structures and algorithms, we spend time understanding how computers actually work. This might seem like a detour, but it pays dividends. When you understand binary representation, you grasp why certain operations are fast. When you understand memory layout, you see why arrays have O(1) access but linked lists do not. When you understand the call stack, recursion stops feeling magical.
Fundamentals transform algorithmic analysis from abstract notation into something tangible. "O(n) is slower than O(log n)" stops being a rule to memorize and becomes an obvious consequence of how the underlying operations work.
The Path Ahead
This learning journey is structured as a progression, where each section builds on the last.
Fundamentals
We begin at the bottom, with the machine itself. How do computers represent data? How does memory work? What actually happens when your code runs? This foundation makes everything that follows more intuitive.
Data Structures
Next, we explore the building blocks for organizing information. Arrays, linked lists, trees, graphs... each structure has its own strengths and trade-offs. Understanding why a hash table offers O(1) lookups (and when it does not) is more valuable than memorizing the fact.
Sorting Algorithms
Sorting is a classic domain for studying algorithmic thinking. We examine different approaches, from simple quadratic algorithms to efficient divide-and-conquer strategies, always focusing on why each algorithm behaves the way it does.
Algorithm Patterns
Finally, we arrive at the heart of problem-solving: recognizing and applying patterns. Binary search, two pointers, depth-first search, dynamic programming... these are not just algorithms, but ways of thinking. Each pattern is a lens through which entire categories of problems become approachable.
How to Use This Resource
Take your time. Resist the urge to rush through topics or skip ahead to the "good stuff." The fundamentals are the good stuff; they just reveal their value slowly. When you encounter a concept that does not click immediately, sit with it. Return to earlier sections if needed. Understanding compounds over time.
And remember: the goal is not to memorize algorithms, but to develop intuition. When you truly understand a pattern, you can reconstruct it from first principles. That is the kind of knowledge that lasts.