Phases

There are mainly six phases in Data Science Life Cycle

The six phases of data science, commonly followed in a typical data science project, are as follows:

Goal: Clearly define the problem you are trying to solve and the questions you aim to answer using data.

Key Activities:

Understand business or research objectives.

Define success criteria and constraints.

Formulate hypotheses.

Goal: Gather relevant data needed to address the problem.

Key Activities:

Identify data sources (databases, APIs, web scraping, sensors).

Collect structured and unstructured data.

Document data collection methods.

Goal: Ensure the data is accurate, complete, and suitable for analysis.

Key Activities:

Handle missing or inconsistent data.

Remove duplicates and outliers.

Normalize and standardize data formats.

Feature engineering (create or transform features).

Goal: Understand the data and uncover patterns or trends.

Key Activities:

Visualize data using charts and graphs.

Identify correlations and relationships.

Perform statistical analysis.

Generate insights to guide the modeling phase.

Goal: Create predictive or descriptive models using machine learning or statistical techniques.

Key Activities:

Split data into training, validation, and testing sets.

Train machine learning models or fit statistical models.

Evaluate model performance using metrics (e.g., accuracy, precision, recall).

Fine-tune models for optimal performance.

Goal: Deploy the model into a production environment and communicate results effectively.

Key Activities:

Deploy models via APIs, dashboards, or embedded systems.

Monitor model performance in real-world conditions.

Share insights and recommendations with stakeholders.

Document processes and results.

These phases are iterative, meaning you may need to revisit earlier steps based on findings or feedback during the project.