Summary

This repository is a comprehensive handbook for Data Science and Analytics professionals seeking to build a job-winning portfolio. It addresses the need for practical project experience and professional presentation, guiding users from ideation and data sourcing through skill demonstration and portfolio hosting to secure desired roles.

How It Works

The project functions as a curated collection of resources and guides, structured for sequential or targeted use. It details portfolio development stages: planning, data acquisition (listing numerous public and API sources), project ideation (categorized by role/complexity), skill showcasing, tool recommendations, and hosting. The approach emphasizes practical application via diverse project examples, from EDA to ML and visualization.

Quick Start & Requirements

This is a guide, not an installable project. Users begin by navigating the README. Recommended tools include Python (Plotly, Seaborn, Matplotlib), SQL clients (DBeaver), Excel/Google Sheets, visualization tools (Tableau Public), and hosting platforms (Notion). No specific hardware or software prerequisites are mandated for accessing the handbook's content.

Highlighted Details

• Extensive catalog of public datasets and APIs (Kaggle, government, academic, specialized sources).
• Project ideas categorized by role (Data Scientist, Data Analyst) and skill level (Beginner, Intermediate, Advanced), with detailed examples.
• Clear articulation of essential skills for data roles (EDA, A/B Testing, ML, SQL, Visualization).
• Curated recommendations for free/freemium tools for data manipulation, analysis, visualization, and hosting.

Maintenance & Community

Authored by Data Scientist Dawn Choo, with links to her LinkedIn, Instagram, and newsletter. No explicit information on broader community, active contributors, or a formal roadmap is present.

Licensing & Compatibility

The provided README content does not specify a software license, making terms of use, distribution, or modification unclear.

Limitations & Caveats

As a handbook, its utility relies on user initiative for implementation. The repository lacks project code, requiring users to independently source data and develop solutions. The absence of explicit licensing is a significant caveat for usage rights.