Cybersecurity for Data Science

The goal of this course is to help anyone interested in data science understand cybersecurity risks and the tools/techniques that can be used to mitigate these risks. We will review the differences between confidentiality, integrity, and availability, and introduce students to relevant cybersecurity tools and techniques, including cryptographic tools, software resources, and policies that will be needed for data science. We will examine key authentication and access control tools and techniques to help data producers, curators, and users ensure data security and privacy. This course can be taken for academic credit toward CU Boulder's Master of Science in Data Science (MS-DS) program, offered on Coursera. The MS-DS is an interdisciplinary degree that includes faculty from CU Boulder's Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS is a learning outcomes-based, no-application program that is ideal for individuals with broad educational and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.