A 6-part series of hands-on Jupyter notebooks that progressively teaches data science from Python fundamentals through advanced machine learning models including regression, classification, and ensemble methods.
Overview
This series provides a comprehensive, hands-on introduction to data science. Starting with Python essentials, NumPy, Pandas, and statistics, it progresses through web scraping, exploratory data analysis, regression techniques, classification models, and ensemble methods. Each notebook includes exercises with solutions and uses simulated real-world datasets.
Who This Is For
Students, career changers, and professionals looking to build practical data science skills from the ground up
What's Included
- 6 progressive notebooks building from fundamentals to advanced ML
- Python & Statistics Foundations — NumPy, Pandas, descriptive stats, visualization
- Web Scraping & EDA — data collection with BeautifulSoup, cleaning, exploration
- Regression — kNN, linear, polynomial, LASSO, Ridge, cross-validation
- Classification — logistic regression, kNN, confusion matrices, ROC curves
- Ensemble Methods — decision trees, bagging, random forests, boosting