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.
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