Why Data Structures and Algorithms are Important?

Programmers should have a good understanding of data structures and algorithms to write efficient and correct code. There are two important reasons:

• From the perspective of coding interviews: DSA is one of the critical parts of the coding interview. It helps interviewers to evaluate the problem-solving, coding and analytical skills.
• From the perspective of applications: Data structures and algorithms are critical components of several real-life applications and APIs. So programmers should be familiar with the use cases of data structures to write efficient backend code.

A story to understand the importance of DSA!

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 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 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 memorised it and never thought about its performance. Then, he showed me a linear-time solution using dynamic programming.

This opened my eyes because there is a large 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 help us improve performance (independent of systems, hardware, etc).

This was the first time I 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!

Why data structures and algorithms?

While it is important to have a good understanding of programming languages, the real satisfaction comes from having a good 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 8 years of teaching experience with students and professionals, I have identified four reasons to learn data structures and algorithms:

• Help us improve problem-solving skills: Learning DSA helps you develop a systematic approach to solving problems that can be applied to a wide range of situations in computer science.
• Help us improve coding efficiency: By understanding how to use the right data structures and algorithms, you can quickly write code that is more efficient and performs better.
• Enhance our ability to learn new concepts in computer science: Having a strong foundation in DSA makes it easier to learn machine learning, AI, data science, system design, etc.
• Help us succeed in our careers: Many employers place a high value on DSA skills, so having a good understanding of these concepts can make you a more competitive job candidate.

Algorithm is a Technology!

The system 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:

• Does application rely on fast networking? Computer networking heavily relies on routing algorithms.
• Does the application depend on the user interface? The design of the user interface depends on algorithms.
• Does the application require fast hardware? The hardware design uses algorithms.

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.

Algorithms are at the core of Library Functions and APIs

In an age where we use library functions to write code, it may seem unnecessary to learn algorithms and data structures. But it is important to understand the implementation, even when they are hidden behind abstraction. In other words, mastering DSA helps us better understand how to use libraries in code.

• DSA can help us develop a deeper understanding of the logic behind the behaviour of these abstractions. Without this, we may use them improperly or apply them in the wrong way.
• The real knowledge of DSA comes into play when there is a need to design programming libraries, APIs, backend code, machine learning models, high-performance simulations, etc.

Important for Cracking the Coding Interviews

Tech companies test knowledge of data structures and algorithms because they want to hire programmers who efficiently solve complex, large-scale, data-intensive problems. A good understanding of DSA is a clear indicator of this ability. In other words, a programmer proficient in DSA is better equipped to tackle the data-intensive problems that tech companies face.

Algorithms are everywhere in real life!

We can find algorithmic logic in natural patterns and real-life activities (sorting, scheduling, counting, optimisation, etc). As Donald Knuth said, "People who analyse 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." Enjoy learning, enjoy coding, enjoy algorithms!