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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

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

This repo is updated every semester.

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  • Jupyter Notebook 100.0%