Master The DSA Interview [Part 21] - Programming Language Selection

There are a ton of programming languages you can use for a data structures and algorithms interview. This lesson teaches you:

- A basic flowchart to follow to determine which programming language you should use in data structures and algorithms interviews
- Why it's often not worth it to pick up an entirely new but "optimal" programming language for data structures and algorithms interviews
- How overly verbose languages can ruin your chances, explained with an in-depth example

Master The DSA Interview [Part 20] - Missing What Matters

When you're doing a data structures and algorithms interview, you don't have a ton of time. That's why it's important to focus on what matters. This lesson teaches you:

- What order to code your solution in when it comes to happy flow and edge cases
- Why the core data structures and algorithms themselves often aren't that important during the interview
- How to get functionality for free, explained with a real-life Meta interview example
- A tactic you can use to prioritize components of your data structures and algorithms solution properly

Master The DSA Interview [Part 19] - Perfection Obsession

Getting the optimal solution to a data structures and algorithms problem is great, but it isn't always possible. This lesson teaches you:

- Why pursuing the optimal solution upfront when you're very stuck can easily lead to your rejection in a data structures and algorithms interview
- What to do instead when you're unable to get the optimal solution upfront
- How being completely stuck can lead to a vicious cycle that forces you to fail interviews
- A simple way to think about data structures and algorithms problem solving in general

Master The DSA Interview [Part 18] - Fishing

Many engineers think that they got rejected due to the interviewer being bad or "out to get them". While low-quality interviewers do exist (and there are a lot of them), it is often the engineer's fault by being a weak candidate. This lesson teaches you:

- Why there is often a discrepancy between what engineers believe is the cause of their data structures and algorithms interview rejection and what reality actually is
- What fishing is and how it forces interviewers into a very awkward situation that makes them seem icy to the candidate
- How to not do fishing during an interview so you can have a real chance of passing (and make the interviewer more likely to help you)
- Why asking the interviewer for a raw hint is a recipe for failure

Master The DSA Interview [Part 17] - How To Pass FAANG DSA Rounds

Why is it so hard to break into FAANG? Well, it's because FAANG tends to push engineers far harder on data structures and interviews problems. This lesson teaches you:

- The score spectrum FAANG interviewers use to grade candidate
- What it means to get a "Yes" vote on a data structures and algorithms round
- What it means to get a "Strong Yes" vote on a data structures and algorithms round (very important when the market is bad)
- What a strong passing data structures and algorithms interview performance looks like, explained with an in-depth example

What Engineers Get Wrong About Open Source (From the OSS Lead at Meta)

Open source software is not just about the code! It's about understanding and working with project maintainers to solve a problem.

Why Big Tech Companies Do Open Source (From A Director at Meta)

Companies like Meta, Google, Microsoft, and Hugging Face have made enormous contributions to open source. These companies often allocate tens or hundreds of millions of dollars to creating or maintaining open source software. Why? 

- These companies often rely on the software for their production code
- Companies can use popular open-source projects as a tool to recruit top engineers
- Engineers familiar with a company's software will onboard faster

Why Don't More Big Tech Engineers Contribute To Open Source?

The modern software industry runs on open source software. Should more software engineers (especially those in Big Tech) contribute back to open source?

This Facebook Engineering Director Quit To Sail The World

After more than 10 years working at Meta (Facebook), James quit to work on his boat full-time. While sailing, he also contributes to open-source! <https://github.com/tinyplex/tinybase>

Master The DSA Interview [Part 16] - Alternate Approaches

The beautiful (and sometimes frustrating) thing about software engineering is that almost every technical problem has a ton of possible solutions. This lesson teaches you:

- Why being able to come up with alternate approaches is so important as a software engineer at the top level
- How to have a proper discussion with your interviewer about alternate approaches
- What a great alternate approach discussion looks like, discussed with an in-depth example

Dive deeper into real system design here: [\[Course\] System Design Masterclass: Shipping Real Features To Production](https://www.jointaro.com/course/frontend-system-design-masterclass-building-playlists/)

Master The DSA Interview [Part 15] - Edge Cases

Too many engineers only care about the happy flow, but it is the easy thing to do. This lesson teaches you:

- Why handling edge cases is so important as an engineer, especially at FAANG
- The mindset you need to have to tease out edge cases systematically
- Common edge case scenarios for data structures and algorithms problems
- What a great edge case analysis looks like, explained with an in-depth example

Take the code quality course here: [Level Up Your Code Quality As A Software Engineer](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/)

Master The DSA Interview [Part 14] - Clean Code

Just because you're solving a made-up problem doesn't mean that you can write messy code during a data structures and algorithms interview. This lesson teaches you:

- What most engineers don't realize when it comes to code quality, in particular with why it's easier to achieve than they think
- The types of engineers who tend to struggle with code quality
- Common messy code patterns to avoid when coding up a data structures and algorithms solution (and in general)

Take the code quality course here: [Level Up Your Code Quality As A Software Engineer](https://www.jointaro.com/course/level-up-your-code-quality-as-a-software-engineer/)

Master The DSA Interview [Part 13] - You Must Be Fast

Quality is important, but quantity is too, which is mainly demonstrated with velocity. This lesson teaches you:

- Why raw speed is so important with data structures and algorithms interviews in particular
- The quantity bar you need to hit to pass a data structures and algorithms interview the vast majority of the time
- Why interviewers often ask 2 data structures and algorithms problems in a single round
- How an interviewer asking you a harder data structures and algorithms could actually mean that they're trying to save you
- Tips for getting faster at data structures and algorithms

Master The DSA Interview [Part 12] - Stuck? Here's What To Do

A defining trait of a strong software engineer is their ability to conquer hardship, and one of these hardships is getting stuck on a data structures and algorithms interview problem. This lesson teaches you:

- Why you need to communicate when you're stuck
- A proactive foundation you should set up to minimize your chances of getting stuck
- What actions you can take when you're stuck outside of coding up the optimal solution (which you obviously don't have as otherwise why would you be stuck?)

Master The DSA Interview [Part 11] - Space Analysis

**CORRECTION:** Recursive solutions can also have O(n) space for non-binary search style problems where you don't eliminate half of the problem space with each iteration. The classic example here is using recursion to calculate fibonacci numbers.

Run-time analysis is important, but it isn't everything. When it comes to FAANG, run-time on its own can actually be sorely lacking. This lesson teaches you:

- Why space complexity is also important when it comes to data structures and algorithms
- What a great space complexity analysis looks like, explained with an in-depth example
- A common mistake engineers make with space complexity concerning a very common data structures and algorithms pattern

Master The DSA Interview [Part 10] - Run-Time Analysis

After clarifying the problem and coming up with a detailed, granular plan, coding up the solution should have been trivial. Now it's time for big O. This lesson teaches you:

- How to actually learn run-time analysis instead of memorizing the big O of every individual problem
- The structure of a strong run-time analysis for a data structures and algorithms problem
- Exactly what a quality run-time analysis looks like, explained with an in-depth example

Master The DSA Interview [Part 9] - Outline Your Approach

Once you have clarified exactly what you need to deliver with a data structures and algorithms interview problem, you need to figure out how to deliver it. This lesson teaches you:

- How to come up with a data structures and algorithms solution plan
- The many benefits of coming up with a plan proactively
- What a good data structures and algorithms plan looks like, explained with an in-depth example

Master The DSA Interview [Part 8] - Clarify The Problem

Don't just start coding when you get a data structures and algorithms problem in an interview. This lesson teaches you:

- The first thing you should do when getting a data structures and algorithm problem, which is critical for setting you up for success
- Why dealing with ambiguity is so important as an engineer
- The overall question categories you should pull from when clarifying a problem
- How exactly to clarify a data structures and algorithms problem, shown with an in-depth example

Master The DSA Interview [Part 7] - Regularly Be Talking

There is far more to a data structures and algorithms question than simply being able to code up a solution. This lesson teaches you:

- Why consistent communication is critical for data structures and algorithms interviews, especially at top companies
- How much silence is too much in an data structures and algorithms interview
- How bad vs. good engineers solve technical problems in general

Master The DSA Interview [Part 6] - Why Does FAANG Do This?

If data structures and algorithms aren't actually used on the job, why do Big Tech companies use these problems as a cornerstone of their interview process? This lesson teaches you:

- Why Big Tech isn't just being evil and stupid when they use data structures and algorithms problems on their interviews
- How data structures and algorithms problems can have a surprising amount of depth
- What most engineers don't understand when it comes to how FAANG rejects candidates
- How Google and Meta in particular differ from almost all other companies when it comes to hiring, which affects how their interview processes work

If you're interested in working at early-stage startups that don't ask LeetCode problems: ["How to find and connect with scrappy startups?"](https://www.jointaro.com/question/9jFuRj3UymeHsaKIzXia/how-to-find-and-connect-with-scrappy-startups/)

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