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Ashish Kumar | |

Ashish Kumar Data structures and algorithms are the foundation of programming. Algorithms are used to solve problems by utilizing the data stored in data structures. This blog will explain, using examples, why every programmer should learn data structures and algorithms. This article is for people who are just beginning to understand data structure and algorithms and are unsure how much of an impact it will have on their programming skills or career. It is also intended for those who wonder why well-known companies like Google, Facebook, and Amazon hire programmers who are skilled at algorithm optimization. What are Algorithms? Informally, a problem-solving algorithm is nothing more than a list of steps. In essence, they offer a solution. For instance, the algorithm below could be used to solve the factorials problem: Making Your Code Scalable Through the Use of Data Structures and Algorithms Assume that Alice and Bob are attempting to find the sum of the first 1011 natural numbers to solve a simple problem. Alice put the programme into practice while Bob was still creating it, demonstrating how easy it is to criticize Donald Trump. Alice and Bob are overjoyed with how quickly they were able to construct something of their own. Let's enter their workspace covertly and overhear their talk. More on Scalability Scalability is defined as "scale plus ability," which refers to an algorithm's or system's capacity to address problems of greater size. Think about the challenge of organizing a classroom for 50 pupils. Booking a room, obtaining a blackboard, and a few chalks are one of the most basic solutions. What happens, though, if the scale of the problem grows? What would happen if there were 200 students? Your chosen code determines how many instructions you receive, and your computer's hardware and compiler determine how long it takes to run each instruction. The answer is still correct, but additional resources are required. In this situation, a much larger space (perhaps a theater), a projection screen, and a digital pen will all be necessary. What would happen if there were 1000 students? When the problem gets bigger, the solution either doesn't work or requires a lot of resources. This means your solution was not scalable. So what exactly is a scalable solution? Our second solution was far more time-efficient and capable of handling problems of various sizes. The term "constant-time algorithms" refers to them. Example 1: Age Group Problem A slightly modified version of the binary search method can be used to handle problems like discovering persons who fit a specific age range (assuming that the data is sorted). For a detailed explanation on this, refer to the popular data structure course and familiarize yourself with the DSA techniques. Example 2: Rubik's Cube Problem Consider that you are creating a programme to solve a Rubik's cube. 43,252,003,274,489,856,000 positions make up this adorable puzzle, and they are just positions! Consider all the possible routes one could take to end up in the incorrect place. Example 3: DNA Problem DNA is a genetic information-carrying molecule. Roman letters A, C, T, and G are used to symbolize the smaller units that make them up. Consider a job in the bioinformatics industry. Your task is to determine whether a specific pattern appears in a DNA strand. Your method is now 100 times faster because the pattern contains 100 characters. The KMP algorithm would be nearly 1000 times faster if your design included 1000 characters. In other words, if you previously needed 1 second to find the occurrence of a pattern, it will now just take 1 ms. Another way to state this is; you can match 1000 strands of data instead of just one. Final Words In general, software development requires daily technology learning. While utilizing these technologies in one of your projects, you get to learn most of them. Algorithms, on the other hand, do not work that way. You won't be able to tell if the code you are producing right now can be optimized if you don't have a solid understanding of algorithms. You are expected to be aware of them beforehand and to use them when needed. With Learnbay’s DSA training you can master the essential data structures and algorithms concepts for your next tricky tech interview. Why Study Algorithms and Data StructuresDOCX file Informally, a problem-solving algorithm is nothing more than a list of steps. In essence, they offer a solution.
For instance, the algorithm below could be used to solve the factorials problem: |