AI bridge lesson

You completed DSA fundamentals. Continue to AI intro for machine learning pipelines; revisit SciPy intro for sparse graphs and numerical kernels; keep practicing patterns in C++ or Python interviews.

What DSA gave you

• Big-O reasoning for training loops and data loading
• Graphs and heaps for recommendation and search systems
• Hash maps for feature lookups and deduplication
• DP/greedy intuition for sequence and resource problems

Recommended path

1. Python or C++ — language basics
2. NumPy — arrays and vectorization
3. Data Science — workflow
4. DSA (this track) — structures and algorithms
5. SciPy — scientific computing (optional parallel)
6. AI — models and evaluation

Bridge code

#include 
#include 
#include 

int main() {
    std::vector batch = {3, 1, 4, 1, 5};
    std::sort(batch.begin(), batch.end());
    std::cout << "DSA track complete\n";
    std::cout << "Next: /ai/intro and /scipy/intro\n";
    return 0;
}

Important interview questions and answers

1. Q: Why DSA before deep AI?
   A: Understand batch sizes, graph sampling, and why O(n²) feature crosses hurt.
2. Q: SciPy connection?
   A: Sparse LA and optimizers rely on same complexity vocabulary you practiced here.

Self-check

1. Name three skills from this DSA track.
2. Which track covers ML pipelines next?
3. How does SciPy relate to graph/sparse topics?

Tip: Continue at AI intro and SciPy intro—DSA vocabulary applies to training and sparse LA.