“Cat. I This course focuses on model- and data-driven approaches in Data Science. It covers methods from applied statistics (regression), optimization, and machine learning to analyze and make predictions and inferences from real-world data sets. Topics introduced in this course include basic statistics (regression), analytics (explanatory and predictive), basics of machine learning (classification and clustering), eigen values and singular matrices, data exploration, data cleaning, data visualization, and business intelligence. Students will utilize various techniques and tools to explore and understand real-world data sets from various domains. Recommended background: Data science basics equivalent to DS 1010, applied statistics and regression equivalent to MA2611 and MA 2612, and the ability to write computer programs in a scientific language equivalent to a CS programming course at the CS 1000 or CS 2000 level are assumed.”
DS 2010 introduced fundamental linear algebra and stats topics that relate to the data science field. Matrices and their applications were studied in the first half, with different statistical analyses methods for the latter half of the course. I completed a group project with two other students where we created statistical models to solve a data science problem.