Applying Algorithmic Design and Data Structures

 

Object-oriented programming languages rely heavily on algorithms and data structures to solve problems efficiently. Because the types of problems programmers face can vary widely, the right combination of algorithms and data structures also changes depending on the task. Choosing wisely can make the difference between a slow, inefficient program and one that’s elegant and high-performing.

Understanding Algorithms

As Shaffer (2013) explained, “An algorithm is a method or a process followed to solve a problem” (p. 17). In simpler terms, it’s a step-by-step procedure that transforms inputs into outputs. A single algorithm may only solve part of a larger problem, and sometimes several algorithms need to work together to reach a complete solution.

Interestingly, different algorithms can solve the same problem—but not all do so with equal efficiency.

Take sorting, for example:

• Selection Sort compares each element with the rest to find the smallest, moving it to the front. This process repeats until everything is sorted.
• Merge Sort, on the other hand, divides the list into smaller chunks, sorts those chunks, and merges them back together—a divide-and-conquer approach that’s much faster for large datasets.

Both algorithms achieve the same goal, but Merge Sort is typically far more efficient as the data size increases. Algorithms define how a problem is solved, but data structures define how the data is stored and accessed.

The Role of Data Structures

The data structure you choose has a major impact on algorithm performance. Common structures include Lists, Arrays, Stacks, Queues, and Trees—each with unique advantages depending on how data needs to be accessed or modified.

• A Stack uses a last-in, first-out (LIFO) approach. Imagine a stack of books—you can only remove the top book first.
• A Queue uses a first-in, first-out (FIFO) approach, like people waiting in line at a store.

Picking the right structure ensures faster access times, less memory use, and better scalability.

Matching Algorithms and Data Structures

So, which combination is best? It depends entirely on the problem. Here are a few guidelines:

• Small datasets → Selection Sort or Insertion Sort works fine.
• Large datasets → Merge Sort or Quick Sort are more efficient.
• Hierarchical data (like organizational charts or file systems) → use a Tree structure.

Choosing isn’t just guesswork—developers use Complexity analysis to measure efficiency.

Time and Space Complexity

The University of Cape Town (2014) explained that time complexity measures how the number of computational steps grows with input size, while space complexity measures how much additional memory an algorithm uses.

For example:

• Selection Sort has a runtime complexity of O(n²)—its performance slows dramatically as data size increases.
• Merge Sort has a runtime of O(n log n), which scales much better for large inputs.

Understanding these complexities helps you objectively compare algorithms and choose the most efficient one for your needs.

Applying Algorithmic Design in Your Programs

To build efficient, structured programs using algorithmic design and data structure techniques, follow these steps:

1. Define the problem clearly. Know exactly what you’re trying to solve.
2. Identify the data. Determine its type, structure, and access patterns.
3. Choose the right data structure. Match it to your data and access needs.
4. Design and test your algorithms. Focus on correctness first, then efficiency.
5. Analyze performance. Compare time and space complexities to decide which solution best fits your requirements.

When used together effectively, algorithmic design and data structures form the foundation of structured, efficient, and scalable programs.

References

Complexity analysis. (n.d.). Retrieved from http://www.cs.utexas.edu/users/djimenez/utsa/cs1723/lecture2.html

Shaffer, C. A. (2013). Data structures and algorithm analysis. (Edition 3.2). Retrieved from http://people.cs.vt.edu/~shaffer/Book/JAVA3elatest.pdf

University of Cape Town. (2014). Sorting, searching and algorithm analysis. Retrieved from http://python-textbok.readthedocs.io/en/latest/Sorting_and_Searching_Algorithms.html

New to Programming? Me Too — A Beginner’s Guide to Getting Started

    Are you new to programming and do not know how to get started? I felt the same way but recently discovered some helpful ways to get started. Java is an object-oriented programming (OOP) language. It is versatile in that it utilizes the Java Virtual Machine to be platform agnostic, making it unnecessary to compile code separately for Windows, UNIX, Mac OS, among others. It is currently used to develop desktop apps, games, and mobile apps.

    An OOP views real-world objects as things with a collection of states and behaviors. Objects of a similar type or properties are grouped into classes. For example, Cats are a class, and lions, tigers, and tabbies are types of cats that can be created as objects from that class. Examples of states of the cats’ class included size, breed, awake, and sleeping. Examples of behaviors of cats are meowing, roaring, going to sleep, and waking up.

    By creating a class in OOP, the need to repeat code is reduced, as it creates a starting point for building objects. The class can be passed around in the system to improve modularity. It can also protect code by hiding the source code when a class is called upon.

    Getting started is easy. First, download the Java DevelopmentKit (JDK) and install it on your computer. Once installed, you may need to update your ‘Environmental Variables’ to include the path to your Java installation. On a Windows 11 operating system, this can be done in the System Properties on the Advanced tab as shown in Figure 1. For the Path variable, add the file path to the bin directory of your Java install. For example, C:\Program Files\Java\...\bin.

Figure 1

Environmental Variables for Windows

    Once you have installed the JDK, you can create your first program using Notepad or another text editor application. Copy the following code into Notepad:

public class HelloWorldProgram {

 

   public static void main(String []args) {

      System.out.println("Hello World");

   }

}

 

    Save the file and name it “HelloWorldProgram.java”. Program languages are like other languages, where they have rules that define proper syntax. Take note that the code above is case sensitive, and changing the case for the class and file name can prevent your program from running properly.

    Now that you have saved your program file, it will need to be compiled. To do this, open a CMD prompt and change the directory to where you saved the "HelloWorldProgram.java" file, then run “javac HelloWorldProgram.java". This will create a new .class file in the same directory. Next, enter the command “java HelloWorldProgram” and press enter to run your program. The result should output “Hello World” in the CMD window if you were successful.

    There are additional tools for more advanced programming, such as using an Integrated Development Environment (IDE) like NetBeans. Tools like these can help test and debug your programs as you create them. Additional tutorials and instructions to learn more about programming with Java can be found at TheJava™ Tutorials and Tutorials Point.

    Happy programming! I encourage you to share your programming experiences in the comments, and I look forward to reading about what you have created.