Renaissance Project: The Way to the Peak in CS & Maths

A public, one-year learning plan (Sept. 2026 → Sept. 2027) covering mathematics for machine learning, computer-science fundamentals, data engineering, machine learning, cloud and generative AI, with every weekend's learning applied to AeroLisa, a Skywise-inspired aviation data platform built only on public data. This is an independent learning project, not affiliated with, endorsed by, or connected to Airbus, Skywise or Palantir.

Two repositories, two roles

Repository	Role
Renaissance Project	Learning: plan, weekly exercises, notes, checkpoints, progress
AeroLisa Project	Application: the platform where everything learned here is built and shipped

How it works

• Saturday = Mathematics · Sunday = Computer Science. Full calendar in PLAN.md.
• Each weekend has its own folder in weeks/: topics, checklist, math/ and cs/ work, gaps and notes.
• Four checkpoints 🔍 test everything from memory. Scores are logged honestly in checkpoints/.
• Exercises in cs/ come with pytest tests; CI runs them on every push.

My rules

1. One pushed commit every weekend. The contribution graph is the streak.
2. Minimum floor on bad weekends: one hour and one commit. Never a zero week.
3. First attempt without AI. AI reviews my work; it never writes the first version.
4. 15-minute review every Sunday evening with the template.
5. Cut scope, not dates.
6. Protect rest. At least one free half-day every weekend. Rest is part of the plan.

Phases

Phase	Period	Mathematics	Computer science	AeroLisa
1 · Foundations	Sept.–Nov. 2026	Logic, proofs, combinatorics, graphs, probability	Python, algorithms, data structures, Git, testing	CLI
2 · Data	Nov. 2026–Jan. 2027	Statistics, reliability, linear algebra	SQL, pandas, ETL, Airflow, data architecture	v0.1
3 · Machine learning	Jan.–Mar. 2027	Calculus, Bayes, optimization, inference, PCA	ML models, metrics, anomaly detection	v0.2
4 · TOEIC & industrialization	Mar. 2027	Neural-network math, TOEIC practice	PyTorch, Docker	—
5 · Cloud & GenAI	Apr.–May 2027	Norms, similarity, information theory, attention	GCP, CI/CD, FastAPI, LLMs, RAG, agents	v0.3, v1.0
6 · Interviews	May–Jun. 2027	Interview questions	Portfolio, mock interviews, pitches	Demo

Repository structure

renaissance/
├── .github/
│   ├── workflows/ci.yml         # runs exercise tests on every push
│   └── ISSUE_TEMPLATE/          # weekly review, gap to review
├── checkpoints/README.md        # score log
├── weeks/
│   └── NN_topic/
│       ├── README.md            # topics, checklist, gaps, notes
│       ├── math/                # handwritten scans, notes, solutions
│       └── cs/                  # code + tests
├── PLAN.md                      # full calendar
├── PROGRESS.md                  # weekends, releases, career milestones
├── RESOURCES.md
├── WEEKLY_REVIEW_TEMPLATE.md
└── requirements.txt

Getting started

git clone https://github.com/xdmanflow/renaissance-project.git
cd renaissance-project
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
pytest -q

Author

Manil DOUDOU, engineering student in computer science (AI & Data Science).

License

Code under the MIT License. Notes may be reused with attribution.