Name: Truly Mastering A Data Structures And Algorithms Problem - 2 Sum Example
Uploaded: 2022-04-19T00:42:14+00:00
Duration: 2 min 9 s
Description: Data structures and algorithms are a core part of FAANG software engineer interviews, and they're seemingly the bane of

Truly Mastering A Data Structures And Algorithms Problem  - 2 Sum Example

Why is the following not a good way to remember the solution to 2 Sum?
        
def twoSum(self, nums, target):
    required = {}
    for i in range(len(nums)):
        if target - nums[i] in required:
            return [required[target - nums[i]],i]
        else:
            required[nums[i]] = i

Why is [Look for the difference and use a HashSet] a great way to remember the solution to 2 Sum?

What more general higher-level learning principle is also exercised alongside the proper mastery of data structures and algorithms problems?

Data structures and algorithms are a core part of FAANG software engineer interviews, and they're seemingly the bane of every software engineer's existence. However, so many software engineers don't learn these concepts properly.

Here are the core points from the video:
- In this example, we cover 2 sum, a classic data structures and algorithms problem that many companies use on phone screens. Here's how to learn it properly:
  - **Bad:** Memorizing all the code behind the optimal implementation.
  - **Good:** "Look for the difference and use a HashSet."
- All DSA problems will have this core insight that "unravels" the entire thing. Once you find and truly understand this insight, the implementation should flow naturally.
- This is also a sign of higher learning via "thinking in terms of abstractions". Your brain is a lot more powerful if it is able to take something more singular and high-level and convert it into the nitty gritty. Memorizing everything at the lowest level simply isn't scalable.

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/)
- [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/)

Data structures and algorithms are a core part of FAANG software engineer interviews, and they're seemingly the bane of 

The computer science concept we all love to hate. However, it is vital to master them to pass tech interviews, especially those at Big Tech.

Tech has one of the highest rates of job switching, making this skill incredibly high-leverage and vital to master. Understand what it takes to convince companies you are strongly competent.

[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)