DSA ecosystem preview

DSA connects to language tracks, numeric computing, interviews, and ML. You already have array intuition from NumPy; SciPy uses graph-like sparse structures and O(n log n) FFT—same complexity language.

Related tracks

• C++ intro — STL containers used in playgrounds
• Python intro — same algorithms often in interviews with lists/dicts
• NumPy — vectorized O(n) ops vs Python loops
• SciPy — sparse matrices, optimizers, signal FFT complexity
• AI intro — graphs, heaps, hash maps in training pipelines

Tools you will use here

• std::vector, std::array — dynamic and fixed arrays
• std::stack, std::queue, std::deque — adapter containers
• std::unordered_map, std::set — associative containers
• Manual structs for linked nodes and trees when teaching concepts

STL vector preview

#include 
#include 

int main() {
    std::vector v = {1, 2, 3};
    v.push_back(4);
    std::cout << "size=" << v.size() << " last=" << v.back() << "\n";
    return 0;
}

Important interview questions and answers

1. Q: NumPy vs std::vector?
   A: Both offer contiguous storage; NumPy adds broadcasting and dtypes; C++ vector is general-purpose in compiled apps.
2. Q: Why link SciPy?
   A: Sparse graph algorithms and FFT are interview favorites tied to real scientific code paths.

Self-check

1. Name three related tracks besides DSA.
2. What STL container grows with push_back?

Tip: Same patterns appear in Python lists/dicts—C++ STL names differ, concepts do not.