DSA Studio

Foundations that stick.

Structured lessons, readable Go solutions, and a test runner that mirrors real interview platforms.

Learning Tracks

Build six cumulative compiler milestones: expression trees, a BC1 virtual machine, BC2 closures and GC, control flow, linked modules, and a bounded static type checker. Requires Go, recursion, maps, and stack tracing; each module pairs worked artifacts with independent debugging, transfer, and diagnostic review.

Practice advanced data structures and algorithms in Go or Python, then reason through system-design scenarios. Requires basic complexity analysis, recursion, maps, and queues; use module lessons to connect invariants to implementation.

Use task-based checklists and measurement experiments to investigate algorithm and implementation performance in Go and Python. Best used alongside a working solution; choose the section matching the bottleneck you are investigating.

Build ML foundations through Python list implementations: mathematical checks, saved representations, stable classifiers, evaluation, PCA, and XOR. Start with Python and algebra; module readiness guides provide classifier, text, and neural-network routes into DL v2.

Build deterministic PostgreSQL queries through worked row traces, four implementation exercises, and two reasoning checkpoints. Requires basic SELECT, joins, grouping, and timestamps; teaches windows, as-of snapshots, and bounded recursive CTEs.

Practice 150 Go problems across arrays, trees, graphs, search, and dynamic programming with pattern lessons and checkpoints. Requires basic Go syntax and data structures; reconstruct the reasoning before tackling each module’s problem set.

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