Data Science Workflow
📘 Data Science
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📅 Nov 14, 2025
⏱ Estimated reading time: 1 min
Introduction
The Data Science workflow describes the step-by-step process followed to solve a real-world problem using data.
1. Problem Definition
Identify the business problem. Example: Predict customer churn.
2. Data Collection
- Databases (SQL)
- APIs
- Web scraping
- Sensor or log data
3. Data Cleaning
Remove duplicates, handle missing values, fix errors.
4. Exploratory Data Analysis (EDA)
- Understand data distribution
- Detect outliers
- Find relationships between features
5. Feature Engineering
Create new useful features from raw data.
6. Model Building
Use algorithms like Linear Regression, Decision Trees, KNN, etc.
7. Model Evaluation
- Accuracy
- Precision & Recall
- Confusion Matrix
- ROC-AUC Score
8. Deployment
Deploy using Flask, FastAPI, AWS, Azure, or Docker.
Conclusion
The workflow ensures a structured way to deliver reliable Data Science solutions.
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