We often get asked about the best language to learn data structures and algorithms, and whether the language you choose makes a difference.
There are hundreds of programming languages and many would-be developers feel paralyzed by the vast amount of choices. It’s easy to spend too much time thinking about how to learn rather than putting pen to paper.
The best language to learn data structures and algorithms is the one you are currently using. There isn’t a right or wrong language to pick for learning data structures and algorithms. Algorithms are generally language-agnostic so there is no need to pick up a new language specifically to learn algorithms. If you haven’t learned a programming language yet, plenty of developers recommend a low-level language like C++.
Let’s explore the different choices you have in terms of learning data structures and algorithms. The approach you take is going to be down to preference and depend on the career you want.
What is the best language to learn data structures and algorithms?
Generally speaking there isn’t one single best language to learn data structures and algorithms. You want to approach algorithms in a way that the knowledge can be learned once and transferred over to other languages in the future. Pick the language with which you are most familiar to make the process easier.
Whatever option you go with, just remember not to try and learn too many languages at once. Narrowing your focus will allow you to focus on what is important.
C++ comes highly recommended as a programmer’s first language. You’ll learn about memory management, time complexity, and fundamental programming principles.
It’s a low-level language so it can be slightly more difficult to pick up but gives you a fantastic base. C++ is a great option to learn data structures and algorithms if you haven’t started programming yet and need a first language. However, don’t learn algorithms before you learn to code, you won’t have any context to work from and it’ll confuse you more.
But if you don’t know a language and aren’t sure of the career trajectory you’d like to take C++ is a great choice. Memory management skills and other benefits like understanding time complexity will serve you well in the long run.
There is a large section of the developer community that believes you should learn data structures and algorithms in a language-agnostic manager. It means you can transfer the skills between whatever languages you learn because you understand the fundamentals.
Using pseudo code can help you understand how an algorithm functions without being attached to a specific programming language. The benefit of doing it this way means you don’t have to have a great depth of language-specific programming knowledge to learn algorithms. Languages like C++ are difficult enough to learn even when you aren’t focusing on algorithm-specific stuff. It’s easy to trip yourself up at the beginning so removing the complexity is hugely beneficial.
However, most algorithms are abstracted out in these languages. The benefit of this is that you can look at the underlying code and understand how the specific method or function modifies data structures, then write your own.
Many developers will argue that the best language to learn algorithms and data structures in is the one you are most comfortable with. Development is not always easy and the first few weeks of a new language are always the hardest.
So why put yourself through it? Use the language you are most comfortable with as a base for your learning. The benefit of using a language you already know is that you can practice and implement the algorithms. Or for languages where it has been abstracted away, you can take a look under the hood. In contrast, choosing to learn a new programming language means you won’t be able to use them to the best of your ability.
Generally speaking, it allows you to free up time and focus on the problem at hand, instead of wrestling with the specifics of a language.
Learning data structures in different programming languages
You should avoid learning data structures in different programming languages when you first start out because it is confusing. Learning data structures in one language should transfer over to every language if done right, with only the syntactical differences to battle.
Most jobs will require you to have a working knowledge of a couple of languages. So eventually you are going to have to use data structures and algorithms in different programming languages. However, you want to avoid picking up data structures until you have a solid foundation of the second or third language you are trying to learn.
Your best bet is to learn algorithms and data structures in one language incredibly well. Mastering these along with the ins and outs of a specific language will make moving on to another language much easier in the long run. The goal is essentially to be language-agnostic in terms of data structures and be able to apply them across the board without restriction.
The ability to apply data structures across multiple programming languages is a positive thing. However, it should be reserved for developers who have mastered a language already. You don’t want to spread yourself too thin by trying to learn too much at once, especially at the beginning of your career. Get some experience and then build out your skillset from there.
Best places to learn data structures and algorithms
There are some fantastic resources online that offer a comprehensive guide to learning data structures and algorithms in any language. The best place to learn data structures and algorithms will depend on your preferred style of learning. For some people, online courses work best while others prefer books and written content.
Let’s explore the best options for studying data structures and algorithms
- Data Structures Concepts – C – A free tutorial course for picking up the basics of data structures in C. It’s great for those who want to visualize how the algorithms work and is fairly extensive.
- The Deep Dive – Java – You’ll be learning about hashtables, stacks, ques, heaps, sort & search algorithms, and arrays in this comprehensive Java algo course. It’s perfect for the aspiring backend Java developers among you.
- Algorithms Part 1 – Java – You’ll learn all the essentials in the first part of the popular Coursera course including traversal and retrieval. The best part is that it is free.
- Algorithms and Data Structures – Part 1 – This course from Coursera takes a slightly different approach because it tries to be language-agnostic in its approach to teaching data structures and algorithms
- Harvard CS50 – The legendary computer science course is a comprehensive guide to not just algorithms but coding in general. Best of all it is free!
The best language for data structures and algorithms
The best language for data structures and algorithms is the one you are currently using. Learning a programming language is difficult enough without having to compete with also learning data structures at the same time. Avoid the difficulty of learning by using a language that you already know.
It is worth noting that you don’t always need to know data structures and algorithms to be a successful developer. Plenty of web developers never get a deep understanding of DSAs and do well. However, plenty of big companies test against them as part of their rigorous recruitment process, so if you want to work for a big company you will need to know them.
There isn’t a single best language to learn data structures and algorithms but your best bet is to stick to one language. Ideally, choose a language that you are well versed in or one that you intend to use in your future career. The best language is the one that is the most appropriate for you, there is no point in learning an entirely new language just for data structures and algorithms,
Nathan Britten, the founder and editor of Developer Pitstop, is a self-taught software engineer with nearly five years of experience in front-end technologies. Nathan created the site to provide simple, straightforward knowledge to those interested in technology, helping them navigate the industry and better understand their day-to-day roles.