Concept:
The Foundational Methodology for Data Science is a structured framework that guides data scientists through the lifecycle of a data science project — from problem definition to deployment and continuous improvement.
Step 1: {\color{red}Business Understanding}
This stage defines the problem from a business perspective:
- Identify objectives and goals
- Understand stakeholders’ needs
- Define success criteria
Step 2: {\color{red}Analytic Approach}
Determine the appropriate analytical technique:
- Classification, regression, clustering, etc.
- Choose methods based on problem type
Step 3: {\color{red}Data Requirements}
Specify the type of data needed:
- Structured or unstructured data
- Data sources and formats
Step 4: {\color{red}Data Collection}
Gather the required data from:
- Databases, APIs, surveys, logs
- Internal and external sources
Step 5: {\color{red}Data Understanding}
Explore and analyze the collected data:
- Identify patterns and anomalies
- Perform exploratory data analysis (EDA)
Step 6: {\color{red}Data Preparation}
Clean and transform data for modeling:
- Handle missing values
- Normalize and encode variables
- Feature engineering
Step 7: {\color{red}Modeling}
Build predictive or analytical models:
- Select algorithms
- Train models using prepared data
Step 8: {\color{red}Evaluation}
Assess model performance:
- Use validation metrics (accuracy, precision, RMSE)
- Compare multiple models
Step 9: {\color{red}Deployment}
Implement the model in real-world systems:
- Integrate into applications or dashboards
- Enable real-time or batch predictions
Step 10: {\color{red}Feedback and Monitoring}
Continuously improve the solution:
- Monitor model performance
- Collect user feedback
- Retrain models as needed