Algorithms For Big Data Compsci Net Worth 2026: Salary, Income & Wealth

Logistics, course topics, basic tail bounds (Markov, Chebyshev, Chernoff, Bernstein), Morris'

Khintchine, decoupling, Hanson-Wright, proof of distributional JL lemma.

Oblivious subspace embeddings, faster iterative regression, sketch-and-solve regression.

Low-rank approximation, column-based matrix reconstruction, k-means, compressed sensing.

Amnesic dynamic programming (approximate distance to monotonicity).

ℓ1/ℓ1 recovery, RIP1, unbalanced expanders, Sequential Sparse Matching Pursuit.

Communication complexity (indexing, gap hamming) + application to median and F0 lower bounds.

External memory model: linked list, matrix multiplication, B-tree, buffered repository tree, sorting.

Linear least squares via subspace embeddings, leverage score sampling, non-commutative Khintchine, oblivious subspace ...

Approximate matrix multiplication with Frobenius error via sampling / JL, matrix median trick, subspace embeddings.

Randomized and approximate F0 lower bounds, disjointness, Fp lower bound, dimensionality reduction (JL lemma).

MapReduce: TeraSort, minimum spanning tree, triangle counting.

Competitive paging, cache-oblivious