In this video we welcome you to our data science content.

We present two options for organizing the work you do in this course:

1. Copying the notebooks are working on to a new folder.
2. Creating a local branch of the repo where you do your work.

We discuss:
1. A parametric supervised learning framework.
2. Single and Multiple Linear Regression.
3. Data Splits

We give several perspectives on regularization techniques including:
1. Ridge and Lasso as MAP estimators.
2. Ridge as OLS with "pseudo-observations"
3. Ridge as a "smooth" version of PCA regression.

In the first week of Math Hour we:
(1) Introduce a Framework for Parametric Supervised Machine Learning
(2) Discuss Maximum Likelihood Estimation (MLE)
(3) Discuss Maximum A Posteriori Estimation (MAP)

We cover 4 methods of collecting data:
1. Data Source Websites
2. Web scraping with BeautifulSoup
3. APIs
4. Using SQLAlchemy to interact with SQL databases.

We give a geometric account of the Singular Value Decomposition of a matrix. The SVD of the centered design matrix gives Principle Component Analysis.

We discuss:
1. The Bias/Variance Tradeoff
2. Regularization
3. Principle Component Analysis
4. Feature Selection Approaches

This video will walk you through cloning the GitHub repo. It also addresses how to troubleshoot some common pitfalls.

1. We give a geometric interpretation of Bessel's correction
2. We derive MLE estimates for simple linear regression.
3. We interpret multiple linear regression as orthogonal projection.

We discuss categorical variables, non-linear transformations, scaling data, pipelines, and residual plots.

Introduction to time series, including adjustments to cross validation, dates and times in Python, baseline models, rolling average forecasts, and exponential smoothing forecasts.