Top Data Science Tools — By Function 📊

A quick view of the tools commonly used across the data science workflow:

🔹 Data Collection
• Scrapy, BeautifulSoup – Web scraping
• APIs – External data access
• Selenium – Dynamic scraping
• Google BigQuery – Large-scale data ingestion

🔹 Data Cleaning & Processing
• Pandas – Data manipulation
• NumPy – Numerical computing
• OpenRefine – Data cleanup
• Excel – Basic cleaning & formatting

🔹 Modeling & Machine Learning
• Scikit-learn – Classical ML
• TensorFlow – Deep learning
• PyTorch – Research-friendly DL
• XGBoost – Gradient boosting
• Keras – Neural network APIs

🔹 Deployment
• Docker – Containerization
• Kubernetes – Model scalability
• FastAPI – ML APIs
• AWS SageMaker – End-to-end ML deployment
• MLflow – Experiment tracking

🔹 Visualization & BI
• Matplotlib, Seaborn – Statistical plots
• Plotly – Interactive charts
• Tableau, Power BI – Business dashboards

👉 Tools change, but knowing when and why to use them matters more than how many you know.