“Cat. I This course introduces core methods in Data Science. It covers a broad range of methodologies for working with large and/or high-dimensional data sets to making informed decisions based on real-world data. Core topics introduced in this course include data collection through use cycle, data management of large-scale data, cloud computing, machine learning and deep learning. Students will acquire experience with big data problems through hands-on projects using real-world data sets. Recommended background: Data science basics equivalent to DS 1010, and data analysis principles and modeling equivalent to DS 2010, knowledge of basic statistics equivalent to (MA2611 and MA 2612), and the ability to program equivalent to (CS 1004 or CS 1101 or CS 1102) and (CS 2102, CS2103 or CS 2119), as well as understanding of databases equivalent to (CS3431 or MIS3720) are assumed.”
DS 3010 focused on more programming ideas for data science. Python was used to work on several concepts, with a case study each week. Topics such as machine learning with scikit- learn utilizing algorithms like KNN and Linear Regression were covered. Also topics like cloud computing, data management, and deep learning were introduced.