Thinking About Algorithms and Data Structures Like a Newbie
When I first heard terms like algorithmic design and data structures, they sounded scary. However, once I started breaking them down, I realized it is just about having a smart plan before you start coding. Instead of jumping straight into writing code, you think through the steps your program needs to take and how you will organize the data. This kind of planning helps build structured programs. Structured programs are organized, easy to read, and easier to fix or update later (W3Schools, n.d.).
Algorithmic design is about figuring out the steps your program needs to follow to get the right result. Data structures are the ways you organize and store the data so that your program can use it efficiently.
Why Some Designs Work Better Than Others
Not every algorithm or data structure fits every situation. Some are better because they solve problems faster. For example, using a simple list to find something in a long set of data might work, but you may have to check each item one by one. This can take a long time. A binary search on a sorted list is faster because it splits the list in half again and again until it finds what you need (Schaffer, 2013; GeeksforGeeks, n.d.).
Big O notation is a way to describe how fast or slow an algorithm is as the input gets bigger (Lysecky, 2015; University of Texas at San Antonio, n.d.; GeeksforGeeks, n.d.). Understanding this helps you choose better designs. For example, a linear search has a time complexity of O(n), which means it gets slower as the list grows. A binary search has O(log n), which is much faster for large lists. Learning this helps you pick the right approach and save time (GeeksforGeeks, n.d.).
The type of data structure you choose also matters. If you need to add and remove items often from both ends of a list, a deque might be better than a normal list. If you need to quickly check if something exists, a hash set can be faster than searching through a list. Choosing the right data structure is part of algorithmic thinking. It is about using the right tool for the job (GeeksforGeeks, n.d.).
How I Would Actually Apply This
If I were building something simple, like a task manager, I would think first about the structure. A queue works well because it handles “first in, first out” order. Then I would make an algorithm that takes the first task, completes it, and moves to the next until the list is empty. This is algorithmic thinking and structured design working together.
For something more complex, like managing contacts in an app, I might use a tree structure. Trees organize data in levels, which makes searching or sorting faster than using a simple list. Then I would use an algorithm to search or sort the tree efficiently (GeeksforGeeks, n.d.).
The main idea is to match the right data structure and algorithm to the work your program needs to do. This is how you build structured programs that run smoothly, use resources wisely, and are easier to maintain. Algorithmic design and data structures are tools that make your code smarter, faster, and more reliable.
References
GeeksforGeeks. (n.d.). Time and space complexity in data structures. Retrieved from https://www.geeksforgeeks.org/time-and-space-complexity-in-data-structure/
Lysecky, R. (2015). Data structures essentials. zyBooks.
Schaffer, C. A. (2013). Data structures and algorithm analysis (3.2 Java Version). Retrieved from https://people.cs.vt.edu/~shaffer/Book/JAVA3elatest.pdf
University of Texas at San Antonio. (n.d.). Complexity analysis, data structures: Lecture 2. Retrieved from https://www.cs.utexas.edu/~djimenez/utsa/cs1723/lecture2.html
W3Schools. (n.d.). Structured programming. Retrieved from https://www.w3schools.com/structured_programming/index.php
No comments:
Post a Comment