Looking at the hiring trends of tech companies, data structures and algorithms play a crucial role. But why are data structures and algorithms important in an interview?Most people are already familiar with the concept: interviews can be high-pressure situations, and solving algorithmic problems on the spot demonstrates a candidate's ability to think logically and solve problems under pressure. It can also help companies assess a candidate's ability to analyze problems, design efficient algorithms, and write error-free, clean**.
Some programmers believe that data structures and algorithms are only important during interviews and are useless afterward. This is not entirely the right view!Let's look at this from a different perspective.
Most of the capabilities of big tech companies are often run on massive amounts of data (i.e., billions of dollars). On the other hand, they build most of the features in-house. Therefore, the efficient ** logic of all these features is essential to perform well under heavy loads and provide a better user experience. In simple terms, efficient algorithms are a key necessity for such large companies to achieve these functions.
With an effective algorithm, it will significantly increase throughput. But if the implementation is inefficient from the start, they will need to invest time and resources to address this performance bottleneck later. However, if they have skilled engineers who know how to write efficiently initially, they can save time and resources and focus on other critical aspects such as architecture, system scaling, availability, reliability, security, and more.
The performance of the system depends on the selection of efficient algorithms and the selection of fast hardware. Even applications that don't need to use algorithms directly at the application level rely heavily on algorithms.
Learning Xi use algorithms and data structures to solve problems and design high-performance applications is one of the key industry skills. It can be found everywhere in computer science, including computer graphics, machine Xi, image processing, cryptography, game design, data analytics, high-performance computing, and many more.
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