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📚 Professional Data Science Resource Masterlist
This document provides a curated list of high-quality resources to supplement your practice notebooks and your DataScience Learning Hub.
🏎️ Core Tool Cheatsheets (PDFs & Docs)
- NumPy: Official Cheatsheet — Arrays, Slicing, Math.
- Pandas: Pandas Comparison to SQL — Essential for SQL users.
- Matplotlib: Usage Guide — Anatomy of a figure.
- Scikit-Learn: Choosing the Right Estimator — Legendary Flowchart.
🧠 Theory & Concept Deep-Dives
- Stats: Seeing Theory — Beautiful visual statistics.
- Calculus/Linear Algebra: 3Blue1Brown (YouTube) — The best visual explanations for ML math.
- XGBoost/Boosting: The XGBoost Documentation — Understanding the math of boosting.
🏆 Practice & Challenges (Beyond this Series)
- Kaggle: Kaggle Learn — Micro-courses for specific skills.
- UCI ML Repository: Dataset Finder — The best place for "classic" datasets.
- Machine Learning Mastery: Jason Brownlee's Blog — Practical, code-heavy tutorials.
🛠️ Deployment & MLOps
- FastAPI: Official Tutorial — Deploy your models as APIs.
- Streamlit: Build ML Web Apps — Turn your notebooks into beautiful data apps.
Note: Always keep your Learning Hub open while you work. It is specifically designed to be your primary companion for these 20 practice modules!