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

**Step 1: Getting Started**
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- Read the :doc:`Quick Start Guide <../quickstart>` to install edaflow and run your first workflow.
- Review the :doc:`Installation Guide <../installation>` for environment setup and troubleshooting.

**Step 2: Data Quality & Cleaning**
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- Study the :doc:`Data Quality <data_quality>` section to learn about missing data analysis, imputation, and outlier handling.
- Practice with example datasets in the :doc:`Examples <../examples/index>` section.

**Step 3: Visualization & Analysis**
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- Explore the :doc:`Visualization <visualization>` guide for distribution analysis, correlation, and advanced plotting.
- Try creating your own visualizations using edaflow functions.

**Step 4: Machine Learning Workflows**
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- Follow the :doc:`ML Workflow Guide <ml_workflow>` for step-by-step model building, comparison, optimization, and evaluation.
- Experiment with classification, regression, and computer vision examples.

**Step 5: Advanced Features & Best Practices**
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- Read :doc:`Advanced Features <advanced_features>` to unlock powerful capabilities for complex projects.
- Review :doc:`Best Practices <best_practices>` to ensure reproducibility and professional standards.

**Step 6: API Reference & Further Exploration**
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- Use the :doc:`API Reference <../api_reference/index>` 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.
