This repository contains materials, assignments, and personalized mini projects for the "Statistical Learning with Python" course offered by Stanford University on edX. The personalized mini projects are designed to showcase the skills learned in each chapter and lab, providing a practical demonstration of the concepts covered in the course.
Welcome to the Statistical Learning with Python repository. This repository contains materials, assignments, and personalized mini projects aimed at applying statistical learning techniques to public policy contexts.
Hello! I'm Rishi, a passionate data scientist with a strong interest in leveraging data science skills for public policy. My goal is to use data-driven insights to inform decisions and shape a more equitable future. I'm currently revisiting and strengthening my foundational knowledge through the "Statistical Learning with Python" course offered by Stanford University on edX.
The "Statistical Learning with Python" course covers key concepts and practical applications of statistical learning techniques using Python. Throughout this course, you will learn about:
- Linear Regression
- Classification
- Resampling Methods
- Linear Model Selection and Regularization
- Non-linear Modeling
- Tree-based Methods
- Support Vector Machines
- Unsupervised Learning
This repository is organized as follows:
- data/: Contains datasets used throughout the course.
- labs/: Lab notebooks and assignments.
- mini_projects/: Personalized mini projects to showcase skills learned in each chapter and lab.
- requirements.txt: Lists the Python packages required to run the notebooks and projects.
- config.ini: Configuration file for storing database connections, API keys, and other settings.
In the mini_projects/ directory, you will find personalized mini projects designed to demonstrate the skills and concepts learned in each chapter and lab. These projects serve as practical applications of the theoretical knowledge gained throughout the course. Each mini project is crafted to reflect my understanding and mastery of the course material, providing concrete examples of how statistical learning techniques can be applied using Python, particularly in the realm of public policy.