Programmers should have a good understanding of data structures and algorithms to write efficient and correct code. There are two important reasons for this:
Before I went to college, I had no experience with computers. Even in my first year, I failed the programming course. I tried asking seniors for advice, but I didn't get a clear answer. I was ready to give up on programming altogether. But in my third year, one of my friends explained the recursive algorithm for finding the nth Fibonacci number and pointed out that it had an exponential time complexity.
This meant that a computer would take years to produce the output for a large value of n. I was surprised and asked why most students wrote this code during their first-year exams. He told me that they just memorized it and never thought about its performance. Then, he showed me a linear-time solution using dynamic programming.
This idea opened my eyes because there is a significant gap between the performance of exponential and linear time solutions. I understood one thing: Algorithms and data structures are everywhere in computer science because they are ideas that help us improve performance by a huge margin (independent of systems, hardware, etc).
This was the first time I truly understood the importance of algorithms. As Steve Jobs said, "You can't connect the dots looking forward; you can only connect them looking backwards." From then on, I loved designing efficient solutions to DSA problems and exploring real-life applications. I even read the book "CLRS" twice!
While it is important to have a good understanding of programming languages, the real satisfaction comes from having a strong command of problem-solving strategies in DSA. The truth is that programming is not just about writing correct code but also about writing efficient code using algorithms and data structures.
After six years of teaching experience with students and professionals, I have identified four compelling reasons to learn data structures and algorithms:
The system's performance depends on choosing efficient algorithms as much as on selecting fast hardware. Even applications that do not directly require algorithms rely heavily on them. For example:
Overall, algorithms are at the core of most real-life applications. Just as rapid innovations are being made in other computer technologies, they are also being made in algorithms.
In an age where we can use library functions to write software code, it may seem unnecessary to learn algorithms and data structures. However, it is important to understand the implementation details, even when they are hidden behind abstraction. In other words, mastering DSA helps us better understand how to effectively use libraries and other abstractions in our code.
Tech companies often test knowledge of data structures and algorithms during the interview process because they want to hire programmers who can efficiently solve complex, large-scale, data-intensive problems.
A strong understanding of DSA is a clear indicator of the ability to devise intelligent solutions for these types of challenges. In other words, a programmer proficient in DSA is better equipped to tackle the data-intensive problems that tech companies face.
We can often find algorithmic logic in natural patterns and real-life activities (sorting, scheduling, counting, optimization, etc). Sometimes, it is concise and straightforward and helps us effectively perform a task.
As Donald Knuth said, "People who analyze algorithms have double happiness. First of all, they experience the sheer beauty of elegant mathematical patterns that surround elegant computational procedures. Then, they receive a practical payoff when their theories make it possible to get other jobs done more quickly and economically."
This quote highlights the dual benefits of studying algorithms: the aesthetic pleasure of discovering elegant patterns, and the practical value of being able to solve problems more efficiently. Enjoy learning, enjoy coding, enjoy algorithms!
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