verified_user
Target Output
Weekly Production Deliverable
Deliverable — end of Phase 1 checkpoint:
DSA: Language basics, patterns, time complexity, concept basics, recursion, sorting, Arrays-I & FAQs (Medium, partial) — done
Core CS: OOPs fully done; DBMS Intro + Data Models/ER + Relational Model/Normalization done
AI/ML: Python fully done via CS50P + Corey Schafer, with 2 from-scratch projects on GitHub; Linear Algebra, Calculus, and Statistics/Probability all covered at "explain it out loud" depth
Aptitude: Quant Modules 1–5, Logical Topics 1–4, Verbal Topics 1–5
Monday
0 / 2 done
DSA
Majority Element-I, Leaders in an Array
1.5h
AI/ML
Calculus Foundations (Intuition)
3Blue1Brown — Essence of Calculus ([YouTube]open_in_new) Ep. 1–4 (derivatives as rates of change, chain rule, product rule visually)
2.5h
Tuesday
0 / 2 done
DSA
Rearrange Array Elements by Sign, Print the Matrix in Spiral Manner
1.5h
CORE
ER Diagrams to Relational Mapping
Entity-Relationship Diagrams; Create ER Diagrams; Relationships in ER Diagram; Relational Models, Intension and Extension, Keys (Primary, Candidate, Super, Foreign) + Quiz
2.5h
Wednesday
0 / 2 done
DSA
Pascal's Triangle I, II, III
1.5h
AI/ML
Calculus to Python & Statistics Intro
3Blue1Brown Ep. 5–7 (implicit differentiation, limits) + Level-up Python check: verify Monday's hand-computed gradient numerically in Python (finite-difference check) + StatQuest & Khan Academy: mean, median, mode, variance, standard deviation
2.5h
Thursday
0 / 2 done
DSA
Rotate Matrix by 90 Degrees, Two Sum
1.5h
CORE
Relational Normalization & Concurrency Foundations
Functional Dependency, Armstrong's Axioms, Inference Rules, Closure of Attributes; Normal Forms (1NF, 2NF, 3NF, BCNF); Denormalization (Denormalisation); Intro to Concurrency, Thomas' Write Rule, Timestamp Ordering Protocol, Conflict vs View Serializability, Serialization Graphs + Quiz
2.5h
Friday
0 / 2 done
DSA
3 Sum
1.5h
AI/ML
Probability & Distributions
Khan Academy & StatQuest: probability basics, normal distribution, central limit theorem, sampling + explain "what is variance" and gradient descent building blocks out loud
2.5h
⚡
Weekend High-Load Execution
• 8.0h Daily Deep Focus Lab
Saturday
0 / 3 done
DSA
4 Sum + revise Pascal's/Spiral/Rotate
1.5h
AI/ML
Probability & Statistics Simulation Lab (4.0h)
Code Monte Carlo simulations in Python to demonstrate Central Limit Theorem, Normal Distribution, and Law of Large Numbers; plot histograms with Matplotlib.
4.0h
APT
Quant M5 & Verbal
Quant M5 Time, Speed and Distance (Basic & Advance) + Verbal: Antonyms and Synonyms (Basic & Advance)
2.5h
Sunday
0 / 3 done
DSA
Revision
redo Two Sum/3 Sum/4 Sum without looking at solutions
1.5h
AI/ML
Phase 1 Math & Python Capstone Synthesis (4.0h)
Comprehensive 4-hour review: build a tiny linear regression optimizer using pure math & Python; explain vectors, determinants, and gradient descent out loud.
4.0h
APT
Logical Puzzles & Full Phase 1 Revision
Logical: Puzzles (Basic & Advance) + Full revision self-test: Quant M1–5, Logical L1–4, Verbal V1–5
2.5h
edit_note
Week 05 Technical Notes & Journal
#LeetCode
#Java
#DSA
#Week05