How to Apply Algorithms and Data Structures in Programming

 

When I first started learning to code, I thought programming was just about writing instructions to make the computer do what I wanted. And at first, that’s true—just getting your code to run feels like a win. But as I kept learning, especially in structured programming courses, I began to realize how important it is to think about how your code runs, not just that it runs. That’s where algorithms and data structures come in. These concepts are more than textbook terms—they’re essential tools for writing clean, efficient, and scalable code.

An algorithm is basically a step-by-step plan to solve a specific problem. You can think of it like a recipe that the computer follows exactly. A data structure is the way we organize and store the data that the algorithm operates on. When you're working on small programs, you might not notice the difference a good algorithm or data structure makes. But as your projects grow or the data gets bigger, bad decisions can really slow things down or make your code harder to maintain. For example, let’s say you need to search for a name in a list. If the list is unsorted and you check each item one at a time, that’s called a linear search, and it takes more time as the list gets longer. But if the list is sorted, you can use a binary search, which is much faster and more efficient. According to Lysecky et al. (2015), understanding the differences in algorithm efficiency—especially concepts like Big-O notation—is key to writing better programs.

In one of my own projects—a basic to-do list app—I started applying these principles. I used an array to store tasks because I needed to access them by index. Then I implemented a priority system to highlight urgent tasks, which led me to look into using a priority queue. At first, I was searching for tasks using a simple loop, but I quickly realized that if I wanted to scale the app or allow users to search by category, I’d need a more efficient approach like a hash map. Each of these decisions impacted how the program performed and how easy it was to add features later on. I wasn’t just writing code that worked—I was building something that made sense and ran efficiently.

There’s also the question of whether some algorithms and data structures are better than others. The short answer is yes, but it depends on the problem you're solving. Arrays are great for accessing data quickly by index, but not so great for inserting or deleting elements in the middle. Linked lists handle insertions and deletions better but are slower when accessing by index. A hash map allows for fast lookups by key, which is perfect when you’re working with unique identifiers. Choosing the right data structure and pairing it with the right algorithm can drastically improve how your program runs and how easy it is to maintain or expand later.

What I’ve learned is that understanding algorithms and data structures isn’t just about passing a course or checking off a learning objective—it’s about writing better code. It helps you think clearly, plan your programs, and avoid problems down the road. You don’t need to memorize every type or algorithm, but you do need to understand the strengths and trade-offs of the ones you use. Once you do, you’ll see your code not only work but work well—and that’s when you really start to level up as a programmer.

References

GeeksforGeeks. (2021). Time complexity, space complexity, and the O-notation. https://www.geeksforgeeks.org/analysis-of-algorithms-set-1-asymptotic-analysis/

Lysecky, R., Vahid, F., Lysecky, S., & Givargis, T. (2015). Data structures essentials. zyBooks.

Oracle. (n.d.). Introduction to data structures. Oracle Java Tutorials. https://docs.oracle.com/javase/tutorial/collections/intro/index.html

Educative. (n.d.). What are data structures and algorithms?. https://www.educative.io/blog/data-structures-and-algorithms