canwbu/data-science-fundamentals

Data Science Fundamentals

This repo contains the slides and worksheets for Boston University's CS 506 course and aims to:

1. Centralize all the content for the course
2. Make the content more widely accessible
3. Allow students to get ahead or catch up

Syllabus

0. Course Overview
1. Git / GitHub
2. Clean Code (Engineering Best Practices)
3. Introduction to Data Science
4. Distance & Similarity
5. Clustering (Kmeans)
6. Clustering (Kmeans++ & Hierarchical Clustering)
7. Clustering (DBScan)
8. Clustering (Gaussian Mixture Model)
9. Clustering Aggregation
10. Singular Value Decomposition
11. Latent Semantic Analysis
12. Intro to Classification & K Nearest Neighbors
13. Decision Trees
14. Naive Bayes & Model Evaluation & Ensemble Methods
15. Support Vector Machines (Linear)
16. Support Vector Machines (Non-Linear)
17. Recommender Systems
18. Linear Regression
19. Linear Model Evaluation (Hypothesis Testing)
20. Linear Model Evaluation (Confidence Intervals & Checking Assumptions)
21. Logistic Regression
22. Gradient Descent
23. Neural Networks
24. How to Tune Neural Networks
25. Types of Neural Networks
26. Generative Adversarial Networks

This repo is updated every semester.