Hence I spent the last three months figuring out how to improve my coding interview skills and eventually received offers from big tech companies like Google, Facebook, Airbnb, Lyft, Dropbox and more.

Many of the algorithmic concepts tested in coding interviews are not what I usually use at work, where I am a Front End Engineer (web). Naturally, I have forgotten quite a bit about these algorithms and data structures, which I learned mostly during my freshmen and sophomore years of college.

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I used to think that being able to think, code, and communicate simultaneously was an impossible feat, until I realized that most people are just not good at coding interviews when they first start out. Interviewing is a skill that you can get better at by studying, preparing, and practicing for it.

My recent job search has led me on a journey to improve my coding interview skills. Front End Engineers like to rant about how the current hiring process is broken because technical interviews can include skills not related to front-end development. For example, writing a maze solving algorithm and merging two sorted lists of numbers. As a Front End Engineer myself, I can empathize with them.

Front end is a specialized domain where engineers have to care about many issues related to browser compatibilities, the Document Object Model, JavaScript performance, CSS layouts, and so on. It is uncommon for front-end engineers to implement some of the complex algorithms tested in interviews.

From my experience as an interviewer, most candidates pick Python or Java. Other languages commonly selected include JavaScript, Ruby, and C++. I would absolutely avoid lower-level languages like C or Go, simply because they lack standard library functions and data structures.

One of the top reasons I recommend Python is that it uses consistent APIs that operate on different data structures, such as len(), for ... in ... and slicing notation on sequences (strings, lists, and tuples). Getting the last element in a sequence is arr[-1] , and reversing it is simply arr[::-1]. You can achieve a lot with minimal syntax in Python.

Java is a decent choice too. But because you will have to constantly declare types in your code, it means entering extra keystrokes. This will slow down the speed at which you code and type. This issue will be more apparent when you have to write on a whiteboard during on-site interviews.

The reasons for choosing or not choosing C++ are similar to Java. Ultimately, Python, Java, and C++ are decent choices. If you have been using Java for a while, and do not have time to become familiar with another language, I recommend sticking to Java instead of picking up Python from scratch. This helps you to avoid having to use one language for work and another one for interviews. Most of the time, the bottleneck is in the thinking and not the writing.

If you need to use a data structure that the language does not support, such as a queue or heap in JavaScript, ask the interviewer if you can assume that you have a data structure that implements certain methods with specified time complexities. If the implementation of that data structure is not crucial to solving the problem, the interviewer will usually allow it.

If you have been out of college for some time, it is highly advisable to review the CS fundamentals. I prefer to review it as I practice. I scan through my notes from college and revise the various algorithms as I work on the algorithm problems from LeetCode and Cracking the Coding Interview.

Practice and solve algorithm questions in your chosen language. While Cracking the Coding Interview is a good resource, I prefer solving problems by typing code, letting it run, and getting instant feedback.

There are various Online Judges, such as LeetCode, HackerRank, and CodeForces for you to practice questions online and to get used to the language. From my experience, LeetCode questions are most similar to the questions asked in interviews. HackerRank and CodeForces questions are more similar to questions in competitive programming.

After completing a question on LeetCode, I usually add the time and space complexities of the written code as comments above the function body. I use the comments to remind myself to communicate the analysis of the algorithm after I have completed the implementation.

Learn about and be familiar with the common pitfalls and caveats of the language. If you point them out during the interview and avoid falling into them, you will earn bonus points and impress the interviewer, regardless of whether the interviewer is familiar with the language or not.

Gain a broad exposure to questions from various topics. In the second half of the article, I mention algorithm topics and the useful questions for each topic to practice. Do around 100 to 200 LeetCode questions, and you should be good.

Congratulations, you are ready to put your skills to practice! In a coding interview, you will be given a technical question by the interviewer. You will write the code in a real-time, collaborative editor (phone screen) or on a whiteboard (on-site), and have 30 to 45 minutes to solve the problem. This is where the real fun begins!

Your interviewer will be looking to see that you meet the requirements of the role. It is up to you to show them that you have the skills. Initially, it may feel weird to talk while you code, as most programmers do not make a habit of explaining out loud their thoughts while they are typing code.

However, it is hard for the interviewer to know what you are thinking by just looking at your code. If you communicate your approach to the interviewer even before you start to code, you can validate your approach with them. This way, the two of you can agree on an acceptable approach.

For phone screens and remote interviews, have a paper and pen or pencil to jot down any notes or diagrams. If you are given a question about trees and graphs, it usually helps if you draw examples of the data structure.

Use earphones. Make sure you are in a quiet environment. You do not want to be holding a phone in one hand and typing with the other. Try to avoid using speakers. If the feedback is bad, communication is made harder. Having to repeat yourself will just result in the loss of valuable time.

Many candidates start coding as soon as they hear the question. That is usually a big mistake. First, take a moment and repeat the question back to the interviewer to make sure that you understand the question. If you misunderstand the question, then the interviewer can clarify.

Always seek clarification about the question upon hearing it, even if you think it is clear. You might discover that you have missed something. It also lets the interviewer know that you are attentive to details.

After you have sufficiently clarified the scope and intention of the problem, explain your high-level approach to the interviewer, even if it is a naive solution. If you are stuck, consider various approaches and explain out loud why it may or may not work. Sometimes your interviewer might drop hints and lead you toward the right path.

This is usually the hardest part of the interview. In general, look for repeated work and try to optimize them by potentially caching the calculated result somewhere. Reference it later, rather than computing it all over again. I provide some tips on tackling topic-specific questions in detail below.

Use a good style to write your code. Reading code written by others is usually not an enjoyable task. Reading horribly formatted code written by others is even worse. Your goal is to make your interviewer understand your code so that they can quickly evaluate if your code does what it is suppose to and if it solves a given problem.

Use clear variable names and avoid names that are single letters, unless they are for iteration. However, if you are coding on a whiteboard, avoid using verbose variable names. This reduces the amount of writing you will have to do.

Always explain to the interviewer what you are writing or typing. This is not about reading, verbatim, to the interviewer the code you are producing. Talk about the section of the code you are currently implementing at a higher level. Explain why it is written as such, and what it is trying to achieve.

When you copy and paste in code, consider whether it is necessary. Sometimes it is, sometimes it is not. If you find yourself copying and pasting a large chunk of code spanning multiple lines, it is probably an indicator that you can restructure the code by extracting those lines into a function. If it is just a single line you copied, usually it is fine.

After you have finished coding, do not immediately announce to the interviewer that you are done. In most cases, your code is usually not perfect. It may contain bugs or syntax errors. What you need to do is review your code.

Interviewers like it when you read their minds. What they usually do after you have finished coding is get you to write tests. It is a huge plus if you write tests for your code even before they prompt you to do so. You should be emulating a debugger when stepping through your code. Jot down or tell them the values of certain variables as you walk the interviewer through the lines of code.

If there are large duplicated chunks of code in your solution, restructure the code to show the interviewer that you value quality coding. Also, look out for places where you can do short-circuit evaluation.

Lastly, give the time and space complexities of your code, and explain why it is such. You can annotate chunks of your code with their various time and space complexities to demonstrate your understanding of the code. You can even provide the APIs of your chosen programming language. Explain any trade-offs in your current approach versus alternative approaches, possibly in terms of time and space.

If your interviewer is happy with the solution, the interview usually ends here. It is also common that the interviewer asks you extension questions, such as how you would handle the problem if the whole input is too large to fit into memory, or if the input arrives as a stream. This is a common follow-up question at Google, where they care a lot about scale. 152ee80cbc

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