DataScience

📚 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.

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🏎️ 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.

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Note: Always keep your Learning Hub open while you work. It is specifically designed to be your primary companion for these 20 practice modules!