Learning Path for Data Science with edaflow
===========================================

This section provides a recommended progression for new and aspiring data scientists to master EDA and ML workflows using edaflow.

**Step 1: Getting Started**
---------------------------
- Read the [Quick Start Guide](../quickstart.rst) to install edaflow and run your first workflow.
- Review the [Installation Guide](../installation.rst) for environment setup and troubleshooting.

**Step 2: Data Quality & Cleaning**
-----------------------------------
- Study the [Data Quality](data_quality.rst) section to learn about missing data analysis, imputation, and outlier handling.
- Practice with example datasets in the [Examples](../examples/index.rst) section.

**Step 3: Visualization & Analysis**
------------------------------------
- Explore the [Visualization](visualization.rst) guide for distribution analysis, correlation, and advanced plotting.
- Try creating your own visualizations using edaflow functions.

**Step 4: Machine Learning Workflows**
--------------------------------------
- Follow the [ML Workflow Guide](ml_workflow.rst) for step-by-step model building, comparison, optimization, and evaluation.
- Experiment with classification, regression, and computer vision examples.

**Step 5: Advanced Features & Best Practices**
----------------------------------------------
- Read [Advanced Features](advanced_features.rst) to unlock powerful capabilities for complex projects.
- Review [Best Practices](best_practices.rst) to ensure reproducibility and professional standards.

**Step 6: API Reference & Further Exploration**
-----------------------------------------------
- Use the [API Reference](../api_reference/index.rst) for detailed function documentation.
- Explore additional examples and mini-projects to deepen your skills.

**Tips for Success:**
- Work through each section in order for a structured learning experience.
- Apply concepts to your own datasets for hands-on practice.
- Refer to external resources (scikit-learn docs, statistics tutorials) for foundational knowledge.
- Join the edaflow community for support and collaboration.
