Data Science Life Cycle.
The life cycle of data science typically involves several stages:
1. **Problem Definition:** Clearly defining the business problem or research question that needs to be addressed using data.
2. **Data Collection:** Gathering relevant data from various sources, which may include databases, APIs, sensors, surveys, or other means.
3. **Data Preparation:** Cleaning, preprocessing, and transforming the data to ensure it's in a suitable format for analysis. This may involve handling missing values, outliers, and formatting inconsistencies.
4. **Exploratory Data Analysis (EDA):** Exploring the data to understand its characteristics, identify patterns, correlations, and outliers, and gain insights that may guide further analysis.
5. **Feature Engineering:** Creating new features or transforming existing ones to enhance the predictive power of machine learning models.
6. **Model Development:** Selecting appropriate algorithms and techniques, training machine learning models on the data, and evaluating their performance using various metrics.
7. **Model Evaluation:** Assessing the performance of the trained models on unseen data to ensure they generalize well and meet the desired objectives.
8. **Model Deployment:** Integrating the selected model into production systems or workflows to make predictions or support decision-making.
9. **Monitoring and Maintenance:** Continuously monitoring model performance, retraining models as necessary, and updating them to adapt to changing data or business requirements.
10. **Feedback Loop:** Collecting feedback from model users or stakeholders to improve future iterations of the data science process.
These stages are iterative and often involve revisiting previous steps as new insights are gained or as requirements evolve over time.