1 2 3 # Initialize Otter import otter grader = otter.Notebook("lab10.ipynb") Lab 10: SQL In this lab, we are going to practice viewing, sorting, grouping, and merging tables with SQL. We will explore two datasets: A “minified” version of the Internet Movie Database (IMDb). This SQLite database (~10MB) is a tiny sample of the much larger database (more than a few GBs). As a result, disclaimer that we may get wildly different results than if we use the whole database!

1 2 3 # Initialize Otter import otter grader = otter.Notebook("lab09.ipynb") Lab 9: Probability and Modeling In this lab, you will explore estimators and modeling in two parts: You will explore if the “sample max” is a biased estimator for the true max of a population. Given a sample (and no access to the population), you will bootstrap the sample correlation estimator to infer properties of the population correlation of two features.

1 2 3 # Initialize Otter import otter grader = otter.Notebook("lab07.ipynb") Lab 7: Gradient Descent and Feature Engineering In this lab, we will work through the process of: Defining loss functions Feature engineering Minimizing loss functions using numeric methods and analytical methods Understanding what happens if we use the analytical solution for OLS on a matrix with redundant features Computing a gradient for a nonlinear model Using gradient descent to optimize the nonline model This lab will continue using the toy tips calculation dataset used in Labs 5 and 6.