This repository serves as a curated collection of code snippets and tutorials for data science practitioners, covering topics from Pandas and NumPy to machine learning and visualization. It aims to simplify the data science workflow by providing practical, bite-sized tips and code examples, directly linked to a Substack publication for deeper dives.

How It Works

The repository organizes code snippets by data science domain (e.g., Pandas, Plotting, ML-AI). Each snippet is presented as a link to a Jupyter Notebook, a Substack article, or a blog post, allowing users to explore specific techniques or tools. The content focuses on practical application, often highlighting lesser-known features, performance optimizations, or alternative approaches to common data science tasks.

Quick Start & Requirements

• Clone the repository: git clone https://github.com/ChawlaAvi/Daily-Dose-of-Data-Science
• Requires Python and standard data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib, Plotly). Specific requirements vary per snippet.

Highlighted Details

• Extensive coverage of Pandas, including performance optimization, memory reduction, and advanced methods.
• Numerous tips on Python language features relevant to data science, such as magic methods and f-strings.
• Detailed sections on machine learning concepts, including algorithm explanations, loss functions, and evaluation metrics.
• Focus on data visualization alternatives and best practices for libraries like Matplotlib and Plotly.
• Includes "Cool Tools" and "Run-time Optimization" sections showcasing libraries for debugging, automation, and performance enhancement.

Maintenance & Community

The repository is maintained by Avi Chawla, the author of the "Daily Dose of Data Science" Substack publication. Further community engagement and content updates are primarily driven through the Substack platform.

Licensing & Compatibility

The repository's code snippets are generally provided for educational and personal use. The specific licensing for individual code snippets or associated articles is not explicitly stated in the README, but the open-source nature of the repository suggests permissive usage for learning.

Limitations & Caveats

This repository is a collection of links and code snippets, not a cohesive library or framework. Users must clone the repository and potentially install individual dependencies for each snippet they wish to run. The depth of explanation for each topic varies, with many snippets linking out to external articles for full context.