Week 1: Python Reset + DSA Launch

Sep 15–21  ·  Target times: Easy ≤ 15m · Medium ≤ 30m · Hard ≤ 55m

Daily routine: 75 min DSA (timed) + 10 min syntax typing + 35 min second block. Talk out loud. No AI autocomplete. Over cap → 48h redo queue.

Day 1 — Tue, Sep 15

Python: Lists & comprehensions

Revise (topic checklist)

Note: Drill from memory, no autocomplete. 10 min SyntaxCache/SpeedCoder. Outcome: write list ops + comprehensions without lookup.

Day 2 — Wed, Sep 16

Python: Dicts, sets, collections

Revise (topic checklist)

Note: deque = your BFS/queue workhorse. Outcome: write defaultdict/Counter/deque from memory.

Day 3 — Thu, Sep 17

Python: heapq, strings, idioms + setup

Revise (topic checklist)

Note: Watch NeetCode 'Python for Coding Interviews'. Outcome: heapq + string ops from memory; accounts ready.

Day 4

DSA set 1 of 4

DSA problems (75 min, timed)

Second block (35 min) — System Design + CS: OS Basics
Process vs thread, context switching, concurrency vs parallelism, virtual memory, deadlock (4 conditions). Most important: process vs thread + why concurrency matters. Read OSTEP intro / Neso Academy OS (first 5). Outcome: explain process vs thread + a deadlock scenario.

Day 5

DSA set 2 of 4

DSA problems (75 min, timed)

Second block (35 min) — ML/DL: ML Metrics & bias-variance
Precision/recall/F1, ROC-AUC vs PR-AUC, bias-variance, overfitting, regularization (L1/L2). Most important: PR-AUC vs ROC-AUC on imbalanced data (your medical-imaging domain). Watch StatQuest. Outcome: explain when PR-AUC beats ROC-AUC.

Day 6

DSA set 3 of 4

DSA problems (75 min, timed)

Second block (35 min) — System Design + CS: OS Basics
Process vs thread, context switching, concurrency vs parallelism, virtual memory, deadlock (4 conditions). Most important: process vs thread + why concurrency matters. Read OSTEP intro / Neso Academy OS (first 5). Outcome: explain process vs thread + a deadlock scenario.

Day 7

DSA set 4 of 4

DSA problems (75 min, timed)

Second block (35 min) — ML/DL: ML Metrics & bias-variance
Precision/recall/F1, ROC-AUC vs PR-AUC, bias-variance, overfitting, regularization (L1/L2). Most important: PR-AUC vs ROC-AUC on imbalanced data (your medical-imaging domain). Watch StatQuest. Outcome: explain when PR-AUC beats ROC-AUC.