Graduate School Admission (Waitlist)
Prep Time: 5 months | While working full-time | 2024
Background
When I decided to apply for graduate school, I had five months until the written exam. I was working full-time as an engineer — no leave, no gap year, no room to slow down at work.
The exam covered Data Structures, Algorithms, Operating Systems, Computer Organization, and one or two elective subjects. Each is a full university-level course. The typical preparation window is one year or more.
I had five months.
Strategy
Step 1 — Prioritize ruthlessly
I ran a self-assessment across all subjects: which ones had existing foundations, which were close to zero. I deliberately allocated more time to the weakest subjects rather than spreading evenly.
Data Structures and Algorithms were strong (part of my daily work), so I compressed that time and redirected it to OS and Computer Organization.
Step 2 — High-density learning after hours
Two hours every weekday after work. Six to eight hours on weekends. No video courses — I went straight to textbooks and past exam papers. After each chapter, I immediately tested with past questions. Wrong answers got flagged; flagged questions got priority in the next review cycle.
Step 3 — Simulation sprint in the final month
The last four weeks were pure timed exam simulation: past papers under exam conditions, calculating my answer-speed per subject, and optimizing question order (secure questions first, hard ones last).
Result
I earned a waitlist result. Not an acceptance, but proof that five months of constrained, targeted preparation can produce a meaningful outcome in a domain I entered from scratch.
Takeaway
This made my learning pattern clearer to me: I'm not suited to broad scanning — I'm suited to target-locking and high-density compression. This pattern works especially well in unfamiliar domains because it forces you to find the critical path fast.
It's also the blueprint I'm following for Terraform and AWS certification prep.