all this math and science with the practical business and product concerns you’re working with? These are the sorts of questions we’ll discuss in this sprint. Linear Algebra 40 Hours Linear Algebra is the foundation of nearly all the numerical routines used for practical statistics and machine learning. It’s a deep topic, but this week we’ll learn enough to appreciate how it is used and applied to the many models we’ll learn. Applied Computer Science 1 40 Hours Explore programming and problem solving skills that will prepare you to pass a technical exam and start working on your job search by completing career readiness activities. Linear Models 40 Hours Unit 2 is about Predictive Modeling, also known as supervised machine learning with labeled, tabular data! We can make models to predict continuous numbers, and answer questions like “How much?” or “How many?” This modeling task is called regression. We’ll begin our study of predictive modeling with linear models for regression tasks: ordinary least squares regression, and ridge regression. We can also make models to predict discrete classes, and answer questions like “Is this A or B or C?” This modeling task is called classification. We’ll continue our study of predictive modeling with a linear model for classification tasks, called logistic regression. Kaggle Challenge 40 Hours We’ll continue our study of predictive modeling with tree-based models, such as decision trees and random forests. We’ll also learn how to clean data with outliers, impute missing values, encode categoricals, and engineer new features. This sprint, your project is about water pumps in Tanzania. Can you predict which water pumps are faulty? Applied Modeling 40 Hours For your portfolio project, you will choose your own labeled, tabular dataset, train a predictive model, and publish a web app or blog post with visualizations to explain your model. You will use your chosen dataset for all assignments during the Applied Modeling sprint. You’ll learn how to define machine learning problems, begin the modeling process, choose targets, choose evaluation metrics, and avoid leakage. You’ll improve your model predictions with powerful models like gradient boosting and feature selection techniques like hyperparameter optimization. You’ll improve your model interpretation with insightful visualizations like partial dependence plots and shapley value force plots. Applying predictive modeling to real decisions isn’t easy, but these are the skills employers are looking for! Applied Computer Science 2 40 Hours Explore programming and problem solving skills that will prepare you to pass a technical exam and start working on your job search by completing career readiness activities. Page 41 of 58 REV 10/31/2022 This catalog applies to all students other than those who reside in CA, CO, GA, TX, and DC who have their own catalogs.