What Data Structures and Algorithms topics actually come up in technical interviews?
📰 Reddit r/datascience
Focus on arrays, strings, hashmaps, two pointers, and sliding window for data structures and algorithms in technical interviews for ML roles
Action Steps
- Practice solving easy questions on arrays, strings, and hashmaps using Python
- Move on to medium and hard questions on two pointers and sliding window algorithms
- Focus on understanding the trade-offs between different data structures and algorithms
- Apply your knowledge to solve problems on platforms like Leetcode
- Review and practice consistently to build problem-solving skills
Who Needs to Know This
Data scientists and machine learning engineers can benefit from practicing these topics to improve their chances of passing technical interviews
Key Insight
💡 Focus on key data structures and algorithms to improve problem-solving skills for technical interviews
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💡 Master arrays, strings, hashmaps, two pointers & sliding window for ML technical interviews!
Key Takeaways
Focus on arrays, strings, hashmaps, two pointers, and sliding window for data structures and algorithms in technical interviews for ML roles
Full Article
I’ve been doing a Python Leetcode question a day since more and more companies (especially for ML roles) are including DSA rounds in their DS interviews. My issue is I’m not sure how deep I actually need to go. Right now I’m getting comfortable with easy questions on arrays, strings, and hashmaps, plus two pointers and sliding window on the algorithms side. Should I push further into new topics or just stay in these areas and ramp up the difficulty? <
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