This course focuses on model- and data-driven approaches in Data Science. It covers methods from applied statistics, optimization, and machine learning to analyze and make predictions and inferences from real-world data sets. Topics covered in this course include a brief overview of statistics and linear algebra, followed by introductory machine learning methods such as linear and nonlinear regression, classification, decision trees, and dimension reduction techniques. Data exploration, data cleaning, feature engineering, and the bias-variance tradeoff will also be covered. Students will utilize various techniques and tools to explore and understand real-world data sets from various domains.