Do You Need To Know Data Structures And Algorithms?

As a programmer, you’ve probably spent the early days of your career pouring over code syntax, package libraries and learning the latest in your chosen discipline. But how much do you need to know about data structures and algorithms; and is that knowledge necessary to progress your career? From the inception of the first programming languages to the modern coding languages of today, computer programming has evolved quite a lot. Despite more complexity, more power and efficiency, the fundamental concepts and use of data structures and algorithms in computer programming have not really changed.


You may be unaware of the classical or functional definition of a data structure and algorithm, but it’s almost certain you use them in your everyday life. Data structures and algorithms are a branch of computer science that deals with creating machine-efficient and optimized computer programs. The term Data Structure refers to the storage and organization of data, and Algorithm refers to the step-by-step procedure to drive a desired outcome.

In other words, data structures are a way of cataloguing and indexing data while algorithms are mini-programs independent of the code they reside within.

As an example, consider the metric and imperial system. Let’s suppose we have code thatconverts imperial units of measurement to metric. Values are received in either miles or kilometres and sorted into the correct container. These are your data structures. The algorithm refers to the conversion rate or (Miles (m) = Kilometre (km) x 1.609344)

At its core, DSA is the building block of the software development process. It’s applicable across all programming languages and technologies and the terminology remains pretty much the same between disciplines.


Many people consider Data Structures and Algorithms as just an unnecessary module in their computer science course. DSA is much more than that. It teaches you a way to be a better programmer and a way to think better. It is a skill that will help you throughout your career in some surprising ways.


Data Structures and Algorithms are the fundamentals of Software Development. They remain the same no matter what new technology is used and that puts the focus on the problem rather than the technology in the interview process.


Code is often a delicate tightrope of system resources and intensive data sources. Engineers with a good understanding of data structures and algorithms are good at managing, sorting and storing information. They know efficient techniques needed to do any operation on data. They know which pattern to follow when making data-driven applications. As a result, both the code and system are optimized for increased processing power and efficiency.


Knowing how to access, sort and interpret data is absolutely invaluable as a software developer. Understanding algorithms and how they can process this information efficiently means you will be better prepared to adopt new frameworks and master additional ones.


Engineers who understand data structures and algorithms tend to approach coding problems differently. A pure programmer will look at the problem from a syntax and coding perspective, whereas an engineer with DSA skills tends to look at the big picture. With an understanding of data structures and algorithms, programmers envision how the different elements interact, way- before typing a single line of code. DSA knowledge is a good baseline to gauge a candidate’s ability to problem solve, think analytically and organize information. It’s this creative perspective that often leads to programmers taking on more senior architectural design tasks and having greater influence over technical direction.


Increasingly, tech startups and FAANG are looking for engineers with a strong understanding of Data Structures and Algorithms. It may hurt to hear, but programmers are a dime a dozen these days. Most people can wrap their head around a coding language, but only truly exceptional engineers will understand how to creatively solve problems with analytical focus. Additionally, most tech startups want engineers who can hit the ground running – especially if their engineering org is in very early stages. Resourcing is probably going to be lean so they need people who can contribute from Day 1.


The short answer is no. Many companies are content to hire based on a very specific technical skill and/or experience level. That’s completely fine if that’s what you want, but it’s kind of like learning to play the piano, but only the treble clef notes on the right hand. Sure, you can get by, but it could be so much better.

Being a pure programmer (without DSA knowledge) can work for years, that is, until you encounter a product where you need to be able to scale.

Let’s say you are working on an internal SSO email platform for a small startup and the company decides they want to drastically increase their user base while at the same time adding MFA to their sign-in process. Without Data Structure and Algorithm skills, you’re flying blind when trying to resource to scale up parts of the code. With DSA knowledge, you’re able to determine ahead of time what is feasible and what resources will be needed.

Startups and FAANG typically hire engineers with a solid DSA understanding as they are more likely to innovate and create unique, scalable solutions to new and existing problems.


Not all companies will ask technical questions surrounding DSA. For some, it is implied knowledge, for others, it’s not necessary for your siloed tasks. But high-growth startups and the big tech giants at FAANG will most certainly test your knowledge and problem-solving Skills.

So, what kind of questions pop up in a programming interview? Let’s go through a few basic examples with their answers.

  1.  When is a binary search best applied?
  2. A binary search is most efficient when applied to data that has already been sorted
  3. What is a stack?
  4. A stack is a data structure in which only the top element can be accessed. As data is stored in the stack, each data is pushed downward, leaving the most recently added data on Top.
  5. What is a linked list?
  6. A linked list is a sequence of nodes in which each node is connected to the node following
  7. This forms a chain-like link for data storage.
  8. What is FIFO?
  9. FIFO is a rule used to represent how data is accessed in a queue. “First in First Out” means that data that has been waiting in the queue the longest will be processed first.


Fortunately, there are plenty of resources available to not only master data structures and algorithms, but also prep for practical interviews with tech startups and FAANG. “Cracking the Coding  Interview”, is a book created and compiled by ex-Apple, Microsoft and Google coder, Gayle Laakmann McDowell. Besides the extremely well-structured DSA refreshers, Cracking the Coding Interview includes over 189 programming questions based on algorithm, coding, and design. It’s also a concise guide of how to prep for a FAANG interview and we cover a lot of these tips in one of our next videos.

The 4th edition of the Algorithms textbook by Robert Sedgewick and Kevin Wayne is widely considered in academic circles and universities to be the definitive guide to computer Algorithms.

BeeHyve is a student community with crowdsourced data structures and algorithms resources from around the web. Likewise, GeeksforGeeks features tutorials and examples of high-level Data Structure topics.

Some courses and material can be quite pricey so fortunately there are free alternatives. Udemy offer some free online courses, starting with standard algorithms and gradually moving towards more advanced and real-life algorithmic problems. Instead of regular assignments, this program has about 100 uniquely designed coding problems There’s also a wealth of other online coursework and training that can help bring you back up to speed with DSA.

And while we did say that data structures and algorithms are common across all coding languages, it can benefit you greatly to seek out DSA content modules offered by the community of your chosen programming language. The syntax and language are still dependent on the code they are written on so context is everything. Most online repositories offer deep-dive algorithm courses into languages such as Java, JavaScript and Python.

While there are plenty of programmers who have managed to go through their careers without understanding Data Structures and Algorithms, it’s absolutely worth upskilling if you haven’t already. Not only is thorough knowledge of DSA required for many jobs with startups and FAANG – just having that skill and expertise will make you a much better programmer.