Data Science blends statistics, programming, and business context to turn raw data into decisions. If you’re new, focus on foundations you can apply immediately.
A practical roadmap to get started
- Python refresher: data types, loops, functions, files
- NumPy: arrays, broadcasting, vectorized operations
- Pandas: Series, DataFrame, indexing, groupby, joins
- Visualization: Matplotlib, Seaborn, storytelling
- Basic stats: distributions, correlation, hypothesis testing
Projects that accelerate learning
- Sales insights dashboard: monthly trends, cohorts, and forecasts
- Customer churn analysis: features, baseline model, and insights
- AB test evaluation: lift, significance, and business impact
Keep your projects small and business‑focused. Aim for clear questions and measurable outcomes — that’s what employers value most.