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| # π 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](https://aashishgarg13.github.io/DataScience/)**. | |
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| ## ποΈ Core Tool Cheatsheets (PDFs & Docs) | |
| * **NumPy**: [Official Cheatsheet](https://numpy.org/doc/stable/user/basics.creations.html) β Arrays, Slicing, Math. | |
| * **Pandas**: [Pandas Comparison to SQL](https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_sql.html) β Essential for SQL users. | |
| * **Matplotlib**: [Usage Guide](https://matplotlib.org/stable/tutorials/introductory/usage.html) β Anatomy of a figure. | |
| * **Scikit-Learn**: [Choosing the Right Estimator](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) β **Legendary Flowchart**. | |
| ## π§ Theory & Concept Deep-Dives | |
| * **Stats**: [Seeing Theory](https://seeing-theory.brown.edu/) β Beautiful visual statistics. | |
| * **Calculus/Linear Algebra**: [3Blue1Brown (YouTube)](https://www.youtube.com/@3blue1brown) β The best visual explanations for ML math. | |
| * **XGBoost/Boosting**: [The XGBoost Documentation](https://xgboost.readthedocs.io/en/stable/tutorials/model.html) β Understanding the math of boosting. | |
| ## π Practice & Challenges (Beyond this Series) | |
| * **Kaggle**: [Kaggle Learn](https://www.kaggle.com/learn) β Micro-courses for specific skills. | |
| * **UCI ML Repository**: [Dataset Finder](https://archive.ics.uci.edu/ml/datasets.php) β The best place for "classic" datasets. | |
| * **Machine Learning Mastery**: [Jason Brownlee's Blog](https://machinelearningmastery.com/) β Practical, code-heavy tutorials. | |
| ## π οΈ Deployment & MLOps | |
| * **FastAPI**: [Official Tutorial](https://fastapi.tiangolo.com/tutorial/) β Deploy your models as APIs. | |
| * **Streamlit**: [Build ML Web Apps](https://streamlit.io/) β Turn your notebooks into beautiful data apps. | |
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| > **Note**: Always keep your **[Learning Hub](https://aashishgarg13.github.io/DataScience/)** open while you work. It is specifically designed to be your primary companion for these 20 practice modules! | |