Core Python for data science — lists, dicts, comprehensions, functions

Data manipulation — loading, selecting, filtering, grouping, merging

Numerical computing — arrays, linear algebra, random, broadcasting

Matplotlib, Seaborn, Plotly — charts, distributions, correlations

Descriptive stats, distributions, hypothesis testing, confidence intervals

Regression, classification — train/test, cross-validation, tuning

Clustering, PCA, anomaly detection — discover hidden patterns

Encoding, scaling, selection — transform raw data into model-ready features

ARIMA, Prophet, stationarity, forecasting, seasonal decomposition

R for data science — tidyverse, ggplot2, dplyr, data wrangling