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854c114 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | # 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!
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