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Data Science & Statistical Analysis• 6 min read
The Data Science Lifecycle
Explore the 5 stages of modern data science: acquisition, cleaning, exploratory data analysis (EDA), modeling, and deployment.
What is Data Science?
Data Science combines programming, domain knowledge, and mathematical statistics to extract actionable insights from structured and unstructured data.
Over 80% of real-world data science time is spent on data cleaning, transformation, and validation before any machine learning algorithm is run.
| Stage | Action | Key Tools |
|---|---|---|
| 1. Collection | SQL extract, Web Scraping, API ingestion | PostgreSQL, Requests, Kafka |
| 2. Cleaning | Handling missing values, deduplication, type casting | Pandas, Polars, OpenRefine |
| 3. EDA | Visualizing distributions, correlations, outliers | Matplotlib, Seaborn, Tableau |
| 4. Modeling | Training predictive & clustering models | Scikit-Learn, PyTorch, XGBoost |
| 5. Deployment | Serving via REST API, monitoring data drift | FastAPI, Docker, MLflow |
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Test Yourself With Exercise
Which stage of the data science lifecycle typically consumes the largest percentage of time?