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 to install edaflow and run your first workflow.

• Review the Installation Guide for environment setup and troubleshooting.

Step 2: Data Quality & Cleaning

• Study the Data Quality section to learn about missing data analysis, imputation, and outlier handling.

• Practice with example datasets in the Examples section.

Step 3: Visualization & Analysis

• Explore the Visualization 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 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 to unlock powerful capabilities for complex projects.

• Review Best Practices to ensure reproducibility and professional standards.

Step 6: API Reference & Further Exploration

• Use the API Reference 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.