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Welcome to DS325 Applied Data Science

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)
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

Repository
Open issue

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By Eatai Roth, Gettysburg College

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