Effectively Learning Data Structures And Algorithms (DSA) For Software Engineer Interviews

Which of these is the core building block of data structures and algorithms problems?

True or false: The most effective way to be prepared for data structures and algorithms interview problems is to memorize the code of each problem's solution line by line.

Why should you comment your solutions to data structures and algorithms problems?

True or false: When you are completely stuck, you should just look at the entire solution in order to learn it.

Why is it healthy to struggle on data structures and algorithms problems?

**Data Structure and Algorithm (DSA)** questions are a core part of the interview for most software engineering roles. These questions, sometimes called "Leetcode problems", are essential to master if you want a reasonable pass rate in your interviews. 

However, so many engineering candidates struggle to truly understand these questions, resulting in lower confidence and worse performance. Here are the best tips when it comes to passing these types of interviews:

- The key to mastering DSA is to learn patterns and not memorize code. The "drill and kill" style of studying is really badly suited for DSA.
- 80% of asked DSA problems will use 1 of ~15 different patterns. So rather than focusing on individual problems, your goal should be to connect the problem to a broader, recurring pattern.
- Examples of patterns: binary search, graph traversal, sliding window, tail recursion. Being able to understand problems of the same type is essential. Being able to *name the pattern* is even better. 
- Extensively comment your solutions, primarily with the key pattern behind the problem. When you get started, it will be painstakingly slow to understand each line of code, and potentially comment it. That's ok! You will feel frustrated, but your goal is to deeply understand the problem and solution, instead of simply solving as many problems as you can.
- When you can't figure out a problem, read 10-20% of the solution and see if you can come up with the entire solution from there. Build the muscle of converting high-level points into fleshed out solutions

Related resources:
- ["Mock Interviews vs. Leetcode?"](https://www.jointaro.com/question/U4kohSE8g5LR7GD9cqYH/mock-interviews-vs-leetcode/)
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures And Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/masterclass-how-to-ace-your-big-tech-interview-data-structures-and-algorithms/)
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/masterclass-how-to-ace-your-big-tech-interview-system-design/)
- [How Alex Got Into Meta With 0 Prior LeetCode Experience](https://www.jointaro.com/lesson/ZYiTbN21FsqFxFdpwt5a/how-alex-got-into-meta-with-0-prior-leetcode-experience/)

The Best Programming Languages For Interviews

- Try to use a more concise language like Python if possible. This is because interviews, DSA rounds in particular, are a precise time management exercise.
- However, if you are *very* familiar with a certain language, use that. The exception is if that language is very verbose like C.
- Avoid the trap of using a rarer, "fancier" language. It may cost you time to explain, and if you aren't actually that knowledgeable in it, you may get called out.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

This Tool Is Especially Amazing For Big Tech Interviews

What Big Tech Data Structures And Algorithms Interviews Generally Look Like

FAANG interviews are some of the most well-defined in the industry. However, this style of software engineer interview is often completely failed due to a lack of preparation. Here's what to expect when it comes to data structures and algorithms interviews for Big Tech.

Here are the core points from the video:
- **Phone screens are Leetcode easy and medium** - This will either be 2 easy problems or 1 medium problem. This means that if you haven't progressed to onsite rounds yet, don't spend much time on Leetcode hard problems: That's a premature optimization. Make sure you have your easy and medium problem mastery down, so you can even make it to that final round.
- **Onsites are Leetcode medium and hard** - This will either by 2 medium problems or 1 hard problem. If you receive a problem that's more on the medium side, make sure to budget your time well. There's a good chance the interviewer wants you to solve another problem on top of that.

Related resources:
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures & Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/how-to-ace-your-big-tech-interview-data-structures-and-algorithms-7172021/) - This is the full video this clip is taken from
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/how-to-ace-your-big-tech-interview-system-design-1162021/)

The #1 Tried And True Tool For DSA Interviews

Related resources:

- [\[Taro Top 10\] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [\[Taro Top 10\] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Having The Proper Mindset For Interviews

True or false: You need to care a lot about your Big Tech interview, because passing it will drastically improve your life. The more prominent it is in your brain, the more likely you are to pass.

True or false: Your input is much more important than the output and results you achieve.

Why should you not view interviews as a test?

True or false: You shouldn't take any breaks when studying for interviews, because the relentless focus will help you learn the material better.

- *The more you care about your interview, the less likely you are to pass it.*
- Interviews are all about maintaining a nimble, flexible state of mind, so you can react gracefully when hit with a problem you haven't prepared for. This will almost always happen on interviews with tougher companies.
- If you view your interview as a "make or break" opportunity (which it admittedly very well may be) and go into it with a lot of anxiety, you will not have that healthy state of mind. When your interviewer goes off-script, your mental state will likely shatter.
- View interviews as a chance for you to go in and learn from some very smart people, and if you happen to get a job offer, that's a nice bonus. This much more relaxed and positive mentality will reduce your anxiety.

Related resources:

- [\[Taro Top 10\] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)
- [\[Masterclass\] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/masterclass-how-to-ace-your-big-tech-interview-system-design/)

Avoid This Common Bad Habit That Fails You Interviews

You should allocate enough time to prepare for your data structures and algorithms interview to avoid cramming at the last minute.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Hate Data Structures And Algorithms? Turn It Into A Habit

Set aside 30 minutes, 2-3 times a week, to build a regular habit out of learning data structures and algorithms.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Interview Preparation Is Way Better With Others

Here are the core points from the video:
- Social accountability is a good way to motivate yourself to work hard when studying data structures and algorithms, especially given how miserable studying them can be. Peer pressure works!
- Doing a mock interview with a friend can help you prepare for interviews because you are forced to explain yourself out loud. This process will be more akin to the actual interview process, far more than grinding LeetCode at your computer all day.

If you're looking for a mock interview partner, find one with [Taro Networking](https://www.jointaro.com/networking/): [https://www.jointaro.com/networking/?selectedServices=Mock+Interviews](https://www.jointaro.com/networking/?selectedServices=Mock+Interviews)

Related resources:
- [[Masterclass] How To Manage Your Time Optimally In Tech And Achieve More Results](https://www.jointaro.com/lesson/rnX9EKpzc43TgGW9MozG/masterclass-how-to-manage-your-time-optimally-in-tech-and-achieve-more-results/) - This is the time management session Rahul mentions
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

The Amazing Non-Money Benefits Of Working At Big Tech

DSA Prep Is One Of The Highest ROI Activities On Earth

If you do a good job studying for your data structures and algorithms interview, you can set yourself up financially for the next few years.

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

Struggling With DSA? Don't Worry, That's Healthy!

True or false: Interviewing is all about how much you know. As long as you study a lot and understand enough material, you will pass the interview.

For almost everyone, what does the learning curve look like for data structures and algorithms?

True or false: It's completely normal and even a good sign when you feel dumb while studying data structures and algorithms.

What's the most important piece when you're learning data structures and algorithms material?

True or false: If you aren't making progress on data structures and algorithms after 2 months, your mind probably just isn't geared to understand them. At that point, you should be practical and just look for interviews that don't have these kinds of problems.

Mastering data structures and algorithms (colloquially known as "DSA") is a frustrating experience: It requires such a different type of thinking and is rarely required on the actual software engineering job. This is why if you want to get good at DSA, you need the right mentality first.

Here are the core points from the video:
- DSA learning is an exponential process. You are going to be terrible initially, and eventually things will start to "click" once you hit the inflection point.
- When you feel dumb, understand that this is completely natural: This is almost a good thing as it's entirely expected.
- DSA is very much an input-driven exercise. Build a good process around studying it regularly and commit to it. With constant persistence, you will eventually get to where you want to be!

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

How You Can Supercharge Your Team - Discovering Synergies

What Does A High Trust Team Mean For Code Review?

In this closing section, we focus on the importance of **building and operating within a high-trust engineering team**. Trust is not just a feel-good concept—it directly affects our productivity and velocity when shipping code.

- **We prioritize speed and trust in code review.** When we trust our teammates, we don’t block diffs unnecessarily. Instead, we approve with minor suggestions, knowing the author will follow through responsibly.
- **We use “accept with nits” to keep things moving.** For small, straightforward changes—like renaming variables—we leave a “nit” comment but still approve the diff to avoid introducing unnecessary delay.
- **We avoid slowing each other down over trivial issues.** Each extra cycle of “request changes” can delay code by 1–2 days. We conserve team momentum by trusting competent authors to resolve minor feedback themselves.
- **We treat time as our most precious resource.** Code review should add value without becoming a bottleneck. High-trust practices help us operate with both quality and speed.
- **We maintain expectations through communication.** Even when approving with nits, we clarify, *“Please address before landing,”* so everyone stays aligned on expectations without blocking forward motion.

Code Review MUST Be Fast, Otherwise The Team Is Unhealthy

In this final segment of the video, the speakers reflect on the crucial role that **code review turnaround time** plays in engineering culture, team productivity, and developer happiness. Timely reviews are not just a technical necessity—they reflect the team’s values and operational discipline.

- **We treat code review turnaround time seriously.** Our standard should be to review diffs within *one day*, two at most. Slow feedback cycles—especially taking 4–5 days—compound and slow down overall delivery velocity.
- **We recognize code review as a gatekeeper with real power.** Since all production code must pass through this step, bottlenecks here can deeply affect morale, productivity, and team trust.
- **We advocate for clear team expectations.** When reviews consistently lag, we push for a shared SLA (Service Level Agreement) around review times, ensuring accountability and smoother planning.
- **We balance urgency with respect.** While prompt reviews are key, we also avoid pestering reviewers within minutes of submitting. Giving at least 6 hours (unless it’s urgent) is a sign of maturity and respect.
- **We proactively build good habits.** Many of us block time at the end of our day to batch and respond to review requests, which helps us maintain velocity without excessive context-switching.

Avoid These Junior Engineer Mistakes With Code Review

In this video segment, we reflect on common mistakes and best practices around code submissions, especially relevant to early-career engineers. The discussion centers around how to be thoughtful and intentional in code reviews to earn trust and increase velocity over time.

- **We don’t treat code review as a speed game.** Moving fast at the cost of quality—like deferring fixes or separating tests from the initial diff—often backfires. We strive to bundle fully thought-out changes with tests as one atomic, reviewable unit.
- **We use RFC diffs to gather early feedback.** When we're unsure of direction, we create “Request for Comment” diffs clearly labeled as such. This helps reviewers give high-level input without getting stuck on minor details.
- **We always show proof of testing, even for small changes.** Even if our change is minimal (e.g., a refactor or doc update), we still confirm it compiles and passes existing tests to signal due diligence.
- **We think like reviewers.** Providing context, test signals, and clarity up front not only earns trust but speeds up the feedback loop and helps us land code more efficiently.

3 Ways To Write Deeper, More Impactful Code Review Comments

In this video, we explore how to give more impactful code reviews by moving beyond surface-level feedback and investing in deeper understanding and learning from experienced peers. The focus is on practical habits that help us become more thoughtful, effective reviewers.

- **We rely on automation for style and formatting.** It's not our job to catch syntax or stylistic issues—those should be handled by IDEs and CI systems with pre-agreed style rules.
- **We review code with a clear perspective.** Before reading the diff, we mentally plan how *we* would solve the same problem, which helps us offer more meaningful, constructive feedback beyond just approving what “looks fine.”
- **We learn by watching the best.** One of the fastest ways to improve as reviewers is to observe how senior engineers reject code—what they flag, how they communicate, and the patterns they look for.
- **We replicate good habits.** By studying thoughtful reviewers—like those who consistently flag performance issues like mobile frame drops—we can develop our own review instincts and level up faster.
- **We treat reviewing as an intentional skill.** There are no shortcuts; it takes practice, pattern recognition, and curiosity to become great at it. But by mimicking smart peers, we shortcut years of trial and error.

Don't Forget: Code Review Is Public And Has Huge Consequences

In this final segment, we emphasize how **code review is not just a technical exercise—it’s a public, cultural signal**. Especially for tech leads and senior engineers, the way feedback is delivered in code review has real consequences for team morale, inclusion, and career development.

### Key Takeaways:

- **We treat code review as a public cultural forum**: Comments are visible to others on the team and can impact how engineers—especially juniors or new hires—are perceived. Feedback that embarrasses someone publicly can harm confidence and even affect performance reviews.
- **We avoid harsh criticism in public threads**: If we’re truly concerned about someone’s patterns (e.g., lack of testing mindset or carelessness), we give that feedback in private, not in a code review. We follow the rule: *Praise in public, critique in private.*
- **We understand the leadership signal we send**: As tech leads, our tone sets the bar. If we leave demoralizing or curt comments, we implicitly encourage others to do the same—fostering a negative or toxic culture.
- **We handle serious feedback face-to-face**: When needed, we schedule a quick 15-minute conversation to deliver constructive criticism with nuance and care—ensuring it’s received in the spirit of growth, not shame.
- **We use code reviews to build people up, not tear them down**: Every interaction is a chance to create a safer, stronger engineering culture. We remember that what we say—and how we say it—has lasting impact.

Ultimately, we believe that **code review is leadership in action**—and great leaders use it to uplift their teams, not diminish them.

Don’t Do THIS Kind of Code Review - Healthy vs Unhealthy Comment Example

In this segment, we explore a crucial cultural principle in code review: **“You are not your code.”** The way we give feedback deeply influences team trust, learning, and psychological safety. Our goal isn’t to be “right”—it’s to help each other grow and to build a healthy, collaborative engineering culture.

### Key Takeaways:

- **We critique code, not people**: Everyone—from interns to staff engineers—writes imperfect code. We give feedback without attacking the author, always assuming good intent and shared goals.
- **We use empathy-driven language**: Ending comments with a question mark (“Have you considered…?”) or using tentative language (“This might miss an edge case…”) keeps feedback constructive and opens the door to discussion, not defensiveness.
- **We avoid destructive comments**: Terse feedback like “Did you even test this?” not only fails to help—it can demoralize authors and harm team culture. Being “right” is meaningless if it alienates collaborators.
- **We model humility, especially as senior engineers**: Seniority should amplify empathy, not authority. Even if we're confident, we acknowledge the possibility we're wrong and focus on dialogue, not dominance.
- **We prioritize helpfulness over harshness**: Great reviewers point to specific issues, offer reasoning, and invite collaboration. They focus on moving the work forward—not “winning” a debate.

By reviewing with empathy, humility, and clarity, we turn every code review into an opportunity to strengthen our team and elevate everyone’s craft.

The “Do No Harm” Principle In Coding

In this segment, we discuss the principle of **"Do No Harm"**—a foundational mindset for writing safe, reliable code. While it's great to break large features into smaller diffs, it's essential that each individual diff **stands alone** as non-destructive and does not degrade the product or infrastructure if applied independently.

### Key Takeaways:

- **We ensure every diff is safe in isolation**: Even in stacked diffs, each change must not break functionality or expose incomplete features. Every diff should be self-contained and ideally improve (or at least preserve) the integrity of the codebase.
- **We avoid relying on future diffs to patch behavior**: It's risky to land an unstable or unfinished diff assuming a follow-up will fix it. If that follow-up is delayed or rolled back (e.g., due to a production issue), the earlier diff could introduce bugs or break the app.
- **We use feature gates wisely and early**: When launching features behind flags, we gate the feature at the very beginning—not in later diffs—to avoid exposing half-baked functionality in production.
- **We’re cautious with shared infrastructure**: If a diff touches a shared module or critical system, we proactively reach out to other teams to confirm our changes won’t unintentionally break their workflows or apps.
- **We understand our code lives in a larger system**: Like parts of a giant elephant, changes in one place can affect parts we can’t see. Practicing empathy and caution helps us prevent cascading issues in complex, multi-team environments.

The “Do No Harm” principle reminds us that good engineering isn’t just about adding features—it’s about protecting trust, stability, and the broader system our code lives in.

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