Data Science from Scratch
Dive into Data Science: from Python and Pandas to machine learning. Learn to analyze data with Pandas and NumPy, create visualizations in Matplotlib, Seaborn and Plotly. Study descriptive statistics, hypothesis testing (t-test, ANOVA, p-value, confidence intervals), data cleaning of missing values, outliers and duplicates, plus feature engineering — feature creation, encoding and scaling. Master linear regression with metric interpretation, decision trees (Random Forest, Gradient Boosting) and clustering (K-Means, DBSCAN, hierarchical) on real projects in Jupyter Notebook with SQL queries and reproducible pipelines.