What are the steps in a typical Data Science workflow?

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Steps in a Typical Data Science Workflow

  1. Problem Understanding & Business Objective

    • Clearly define the problem: What are we trying to solve?

    • Understand the business goals and success criteria.

  2. Data Collection

    • Gather data from multiple sources: databases, APIs, logs, web scraping, IoT devices, etc.

    • Ensure data relevance and availability.

  3. Data Cleaning & Preprocessing

    • Handle missing values, duplicates, and outliers.

    • Convert data into a consistent format.

    • Feature encoding (label/one-hot), normalization, or scaling.

  4. Exploratory Data Analysis (EDA)

    • Visualize data (charts, plots) to understand distributions and relationships.

    • Identify key trends, correlations, and potential features.

    • Form hypotheses.

  5. Feature Engineering & Selection

    • Create new features that improve model performance.

    • Select important features using statistical tests or model-based techniques.

  6. Model Building

    • Choose suitable algorithms (regression, classification, clustering, etc.).

    • Train the model on the prepared dataset.

    • Use cross-validation to avoid overfitting.

  7. Model Evaluation

    • Evaluate performance using metrics (accuracy, precision, recall, F1-score, ROC-AUC, RMSE, etc.).

    • Compare multiple models and select the best one.

  8. Model Deployment

    • Integrate the model into production (APIs, dashboards, cloud services).

    • Ensure scalability and performance in real-world conditions.

  9. Monitoring & Maintenance

    • Continuously track the model’s performance.

    • Retrain or update the model as data changes (concept drift).

    • Collect user feedback for improvements.

✅ Interview punchline (short answer):
“A typical Data Science workflow starts with problem understanding, followed by data collection, cleaning, and exploratory analysis. Then we perform feature engineering, build and evaluate models, and finally deploy and monitor them in production to ensure long-term performance.”

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