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/)

Mastering data structures and algorithms (colloquially known as "DSA") is a frustrating experience: It requires such a d

[Masterclass] How To Ace Your Big Tech Interview - Data Structures And Algorithms

Data structures and algorithms (often abbreviated as "DSA") is an infamous staple of the software engineer interview experience. Data structures and algorithms problems are the most well-known for being part of the FAANG interview process, especially for Google.

Here are our Top 5 favorite points from this masterclass (in no particular order):

- **DSA prep is insanely high ROI** - In fact, the return on investment for time spent on DSA studying makes it one of the highest yield activities any human can do period. If you haven't worked at FAANG / Big Tech before, switching into it is likely to increase your pay by 50%+. If you're in the US, this will often mean a raise of $50k+. So the next time you're feeling burnt out from DSA studying, keep this very bright light at the end of the tunnel in mind.
- **Leetcode and Glassdoor are all you need** - DSA studying is a solved problem. You can use other, newer tools if you want, but the old-school ones still work just fine. Glassdoor in particular is naturally very effective given the "Big" nature of "Big Tech". Go through the recent data points from Glassdoor if you're interviewing for Big Tech and make sure you have a good grip on recurring recent problems.
- **Learning DSA is all about pattern recognition** - For 80%+ of DSA problems, there will be some 1-2 liner "trick" that represents the whole solution. For example, the "trick" to 2 sum is "Look for the difference and use a set/dictionary". It is not scalable to memorize solutions as code - Look for patterns and remember solutions as that high-level insight.
- **Communicate, communicate, communicate** - Since FAANG companies are so huge, there are naturally going to be far more stakeholders on every project, making alignment far more important. Because of this, Big Tech companies will often closely judge your communication skills during the interview. Don't just code in silence - Even if you get the right answer, you may still fail for not maintaining a dialogue with your interviewer.
- **Don't forget about space** - Most people are aware of big O run-time complexity, but candidates often forget about space complexity. Make sure to talk about both when you analyze your solution. As a side note, remember that a recursive solution will *not* be O(1) space.

Related resources:

- [\[Course\] Master The Data Structures And Algorithms Interview](https://www.jointaro.com/course/master-the-data-structures-and-algorithms-interview/)
- [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/)
- ["How do I balance my time between studying DSA and fleshing out my portfolio?"](https://www.jointaro.com/question/fhV2SCn56aYF4gfPVLmV/how-do-i-balance-my-time-between-studying-dsa-and-fleshing-out-my-portfolio/)
- ["Should I quit to prep for interviews?"](https://www.jointaro.com/question/Cno3oaZkvUu7BPLhHB7g/should-i-quit-to-prep-for-interviews/)
- [Presentation Slides](https://docs.google.com/presentation/d/13uLUHDvZifjp2aVClGsW29nfuVpQYvrkGLP8cej0R_k/edit#slide=id.p)