Week 3: Linked List, Tries, Trees (pt 1)

Sep 29–Oct 5  ·  Target times: Easy ≤ 12m · Medium ≤ 25m · Hard ≤ 45m

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

DSA set 1 of 5

DSA problems (75 min, timed)

Second block (35 min) — System Design + CS: DBMS + System Design core
SQL vs NoSQL, indexing (B-tree, why it speeds reads), ACID/transactions, normalization; client-server, load balancing, caching (write-through/back, eviction). Most important: indexing + caching. Read System Design Primer (scalability + caching).

Day 2

DSA set 2 of 5

DSA problems (75 min, timed)

Second block (35 min) — ML/DL: CNNs + Transformers basics
Convolution/pooling intuition; self-attention (Q/K/V), why transformers replaced RNNs. Most important: self-attention. Watch StatQuest Transformers + 3B1B attention. Outcome: explain attention simply.

Day 3

DSA set 3 of 5

DSA problems (75 min, timed)

Second block (35 min) — System Design + CS: DBMS + System Design core
SQL vs NoSQL, indexing (B-tree, why it speeds reads), ACID/transactions, normalization; client-server, load balancing, caching (write-through/back, eviction). Most important: indexing + caching. Read System Design Primer (scalability + caching).

Day 4

DSA set 4 of 5

DSA problems (75 min, timed)

Second block (35 min) — ML/DL: CNNs + Transformers basics
Convolution/pooling intuition; self-attention (Q/K/V), why transformers replaced RNNs. Most important: self-attention. Watch StatQuest Transformers + 3B1B attention. Outcome: explain attention simply.

Day 5

DSA set 5 of 5

DSA problems (75 min, timed)

Second block (35 min) — System Design + CS: DBMS + System Design core
SQL vs NoSQL, indexing (B-tree, why it speeds reads), ACID/transactions, normalization; client-server, load balancing, caching (write-through/back, eviction). Most important: indexing + caching. Read System Design Primer (scalability + caching).

Weekend

Project + review
🚀 PROJECT 1 — Recommender (KICKOFF weekend)
Repo + dataset (MovieLens 1M). Baseline: matrix factorization or two-tower embeddings. Get data pipeline + training loop running. Milestone: baseline model training by end of week.
Also: 1 system-design practice out loud. Redo any DSA problem that went over cap (48h queue).