Data Structures & Algorithms

 Algorithmic Design

When developing a program, having a clear idea of how you want it to perform is crucial; that is where algorithmic design comes into play. "An algorithm is a method or a process followed to solve a problem" (Shaffer, 2013). There are many different algorithms to choose from, to search and sort, but the complexity of the design will depend on what you want to accomplish. However, no matter which algorithm you choose, it must perform as intended, have concise steps to perform without ambiguity, and be limited to the least number of steps possible to allow proper execution. You must always keep in mind that the computer running your program will not have an unlimited amount of memory. Time Complexity and Space Complexity are two key concepts to remember. "The time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the input" (Time Complexity and Space Complexity, 2021). Although it is not the same as how quickly a computer may execute the task. Space Complexity is the "amount of memory required by the algorithm to solve given problem" (Time Complexity and Space Complexity, 2021). To get more information about these concepts, click here. Two standard search algorithms are linear search and binary search. A linear search algorithm is a good option for smaller datasets, requiring iteration through the entire dataset until the designated value is found. However, a binary search algorithm is a better option for larger datasets. A binary search will break the dataset into sections by locating the middle value, then shifting left or right depending on where the designated value would fall. The process continues until the value is found while also cutting down on time to complete the task by not iterating through unnecessary values.

Data Structure

Data structures are essential to program performance and can make or break a program. A “data structure is any data representation and its associated operations (Shaffer, 2013). Data structure examples are linked lists, queues, trees, arrays, and stacks. Like algorithmic design, the type of data structure used depends on the task at hand. Linked lists and stacks are two data structures I have had the most success with. Each class allows for adding or removing elements easily with simple methods like add() and remove(). Queues I found a bit confusing using the poll(), peek(), and element() methods. However, practice and repetition will help the concepts stick as with anything new. One thing to note is if you have data likely to change, use a linked list and not an array. Data within an array is fixed, and linked list data is not. 

Going Beyond

When the time comes for me to develop a structured program, I’ll start by figuring out what problem needs solving. From there, I will compare algorithmic design methods while keeping time complexity and space complexity in mind to ensure the program I create suits the task. Lastly, I will determine the best data structure to make my program efficient.

References:

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

Time Complexity and Space Complexity. (2021, July 11). GeeksforGeeks. https://www.geeksforgeeks.org/time-complexity-and-space-complexity/