Intro To Data Science

This course introduces the core concepts of data science and machine learning, covering exploratory analysis, statistical modeling, and predictive algorithms. Students will gain hands-on experience with Python, key libraries (NumPy, Pandas, Scikit-learn, Pytorch), and real-world datasets. By the end, you’ll understand how to build, evaluate, and deploy basic machine learning and deep learning models for practical applications. No prior experience required—just a passion for data!

Key Topics:

Data cleaning & visualization Supervised vs. unsupervised learning Regression, classification, clustering Model evaluation & ethical considerations

Perfect for beginners looking to enter the field or professionals seeking a strong foundation. This course is free of charge and all skill levels welcome!

⚠️ Note: Students must have access to a laptop or desktop with software installation privileges.

What is Data Science, Generative vs Discriminative Models, & Naive Bayes Models

Date: 06.29.2025

Topics:

1. Welcome & Setting Up Your Development Environment
2. What is Data Science?
3. What is a Model?
4. Generative & Discriminative Models - Pros and Cons
5. Math Review: Bayes' Theorem
6. The 'Naive' Assumption
7. The Naive Bayes Model
8. Project: Baby Name Classifier