CS 201 · Year 2 · Semester 1 · 4 credits · Computer Science

Data Structures & Algorithms

Engineered for Scale: Mastering algorithmic efficiency, robust data structures, and dynamic optimization for high-throughput actuarial engines.

Full practice exam · 30 Q · 120 min

Season 1 · 8 episodes

The Millisecond Solvency

When a catastrophic hurricane season pushes NorthPeak Re to the brink of regulatory sanction, a junior actuarial software engineer must rebuild the firm's crumbling 72-hour capital modeling pipeline from the ground up using algorithmic principles before the regulator's final solvency deadline.

Protagonist · Maya Lin, an actuarial developer at NorthPeak Reinsurance with a dual background in financial mathematics and computational complexity.

Setting · NorthPeak Re's European headquarters in Zurich, amidst intense regulatory scrutiny from FINMA auditors and volatile global catastrophe markets.

Stakes · If the overnight risk valuation and catastrophe simulation pipeline cannot complete within a four-hour execution window, the regulator will impose a 450 million euro capital surcharge, forcing the firm to downgrade its credit rating and surrender its primary reinsurance license.

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Episodes

Season finale: Maya deploys the fully refactored, algorithmically optimal capital engine minutes before the FINMA board convene, slashing end-to-end runtimes from 72 hours to 38 minutes and proving solvency under extreme multi-peril scenarios.

From the lecturer: Welcome to CS 201. While modern actuarial platforms abstract away underlying memory layouts, production-grade financial engines, nested stochastic simulations, and million-policy portfolio repricing pipelines demand uncompromising algorithmic discipline. A naive quadratic algorithm on a ten-million policy portfolio will bring an enterprise cluster to its knees; the optimal linearithmic approach finishes before your morning coffee. To succeed in this course, treat code and mathematical analysis as two sides of the same coin. Have blank grid paper and a fine-tip pen ready for every lecture. Before typing a single line of code, you must draw pointer diagrams, trace state invariants, and construct recurrence relations for time and space complexity. We will derive asymptotic bounds rigorously from first principles using limits and recurrence trees, bridge classic algorithms directly to actuarial data structures such as triangle aggregation and claims triage, and benchmark your theoretical Big-O predictions against empirical CPU cycle profiles. Embrace the edge cases, master the invariants, and build computational intuition that scales.

Cold open

At 6:15 AM in Zurich, Maya arrives to find the risk infrastructure cluster frozen at 82 percent completion after 58 continuous hours of compute. The chief risk officer is on the speakerphone: the Swiss regulator FINMA arrives in three weeks, and if the overnight portfolio shock run cannot scale past ten thousand treaties, the desk faces immediate shutdown.

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Transcript

Define Big-O, Big-Omega, and Big-Theta formally using constants and limits
Analyze worst-case, best-case, and average-case runtimes of iterative algorithms using step counts and summations
Solve divide-and-conquer recurrences using the Master Theorem and recursion trees
Evaluate time-space trade-offs in large-scale actuarial simulation and data processing workloads