My Toolkit | Favorite Data Science Tools & Resources

 

My Toolkit: The Essential Tools & Resources That Power My Workflow

In data science, having the right tools can make all the difference. This page is a curated list of the software, libraries, and resources I use daily to learn, code, and build projects. I hope this can be a helpful guide for other aspiring data scientists.

Core Development Environment

• Code Editor: Visual Studio Code - My go-to editor for its flexibility, powerful extensions (like Python and Jupyter), and integrated terminal.
• Version Control: Git & GitHub - Essential for tracking changes, collaborating, and showcasing my work.
• Terminal: Git Bash (on Windows) - Provides a powerful Linux-like command-line experience on Windows.

Data Science & Machine Learning

• Python: The primary language for my data science work.
• Jupyter Notebooks: Perfect for interactive data analysis, prototyping, and storytelling with code and visualizations.
• Pandas & NumPy: The backbone of my data manipulation and numerical analysis workflow.
• Matplotlib & Seaborn: My chosen libraries for creating insightful and beautiful data visualizations.
• Scikit-Learn: My entry point into the world of machine learning for building predictive models.

Learning & Knowledge

• Kaggle: An incredible platform for finding datasets, practicing with notebooks, and learning from the community.
• Coursera & freeCodeCamp: My primary sources for structured learning and high-quality educational content.
• Medium & Towards Data Science: For staying updated with the latest trends, tutorials, and case studies.