Welcome to DS325 Applied Data Science

This book will be a work in progress throughout the semester, a repository for our class notes.

Setting Up and Resources

• 1. Setting Up
• 2. Our course GenAI Policy
• 3. Linear Regression Review Topics
• 4. Review Materials

Regression

• 1. What is a model?
• 2. Linear Regression (Part 1)
• 3. Linear Regression (Part 2)
• 4. Bias, Variance, and Regularization
• 5. Regularization Example: King County housing
• 6. Polynomial Regression

Classification

• 1. Classification
• 2. Logistic Regression
• 3. Trees and Ensemble methods
• 4. Tree Methods (continued)
• 5. Ensemble Methods (finally)
• 6. Forests and Trees
• 7. Principle Component Analysis (PCA)
• 8. PCA: Examples and Observations
• 9. K-Nearest Neighbors (KNN) (a lazy learner)
• 10. K-Means Clustering
• 11. Data Storytelling

Neural Networks

• 1. Neural Networks, Intro and Intuition
• 2. Neural Networks with Keras

Assignments

• Github Submission Guide
• PS00
• PS00 (Solutions by Prof Roth)
• PS01 - (Not so) Simple Linear Regression
• PS02 - Regularization
• Mini-Project 1 (and examples!)
• PS03 - Classification
• Crack the Code!
• ps04 - Group data storytelling
• Extra Credit
• Final Project