Courses
The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.
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CDS DS 598: Special Topics in Media, Arts and Humanities
Coverage of a specific topic in relation to media, arts and the humanities in data science. Topics vary semester to semester. -
CDS DS 599: CDS Research Development Seminar
The first--year doctoral seminar is a required two--semester cohort--based course (4 credits) that must be taken during the first full academic year that a student enrolls in the PhD program in CDS. It is divided into two parts, each providing 2 credits. "CDS Research Initiation Seminar" is offered in the fall semester, and "CDS Research Development Seminar" is offered in the spring semester. The seminar serves three key purposes: 1. It introduces students to the scholarship of (and the rich set of research projects pursued by) the CDS faculty and their guests through colloquia pitched to a multidisciplinary audience. 2. It guides students through the challenging transition into the graduate program in CDS by introducing them to the variety of skills and capacities that are needed to succeed as a scholar. 3. It engenders a sense of community amongst the group of students entering the program as a cohort. 4 cr. Either sem. -
CDS DS 644: Machine Learning for Business Analytics
The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern machine learning techniques through series a lectures and in- class team exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern analytics methods, and the types of problems they can be applied to. The course is open to students with or without a technical background who are interested in analytics. While no prior programming experience is required, students will learn the fundamentals of the R programming language to build and evaluate predictive models. -
CDS DS 657: Law for Algorithms
Algorithms - those information-processing machines designed by humans - reach ever more deeply into our lives, creating alternate and sometimes enhanced manifestations of social and biological processes. In doing so, algorithms yield powerful levers for good and ill amidst a sea of unforeseen consequences. This crosscutting and interdisciplinary course investigates several aspects of algorithms and their impact on society and law. Specifically, the course connects concepts of proof, verifiability, privacy, security, trust, and randomness in computer science with legal concepts of autonomy, consent, governance, and liability, and examines interests at the evolving intersection of technology and the law. Grades will be based on a combination of short weekly reflection papers and a final project, to be completed collaboratively in mixed teams of law and computer and data science students. This course will include attendees from the computer science faculty, students and scholars based at Boston University and UC Berkeley. -
CDS DS 682: Responsible AI, Law, Ethics & Society
The deployment of Artificial Intelligence systems in multiple domains of society raises fundamental challenges and concerns, such as accountability, liability, fairness, transparency and privacy. The dynamic nature of AI systems requires a new set of skills informed by ethics, law, and policy to be applied throughout the life cycle of such systems: design, development and deployment. It also involves ongoing collaboration among data scientists, computer scientists, lawyers and ethicists. Tackling these challenges calls for an interdisciplinary approach: deconstructing these issues by discipline and reconstructing with an integrated mindset, principles and practices between data science, ethics and law. This course aims to do so by bringing together students with diverse disciplinary backgrounds into teams that work together on joint tasks in an intensive series of in- class sessions. These sessions will include lectures, discussions, and group work. This unique course will bring together students from multiple institutions, each contributing undergraduate students from either computing and data science disciplines or from law and public policy disciplines. -
CDS DS 690: Directed Study in Computing & Data Sciences
Directed study in Computing & Data Sciences provides students the opportunity to complete directed research in a selected topic not covered in a regularly scheduled course under the supervision of a faculty member. Student and supervising faculty member arrange and document expectations and requirements. Examples include in-depth study of a special topic or independent research project. -
CDS DS 699: Advanced Topics in Data Science
Various advanced topics in data science that vary semester to semester. Please contact CDS for detailed descriptions. -
CDS DS 990: Computing & Data Sciences Lab Rotation
Experience with translational or applied research pursued in an industrial or laboratory setting is important for in-the-field graduate training. To provide this training, graduate students may complete a lab rotation that provides them with the opportunity to (1) explore lab settings and industrial collaborations that may be relevant to their thesis work; (2) experience the diversity of applied and in-the-field research styles in computing and data sciences; and (3) expand their external network of potential research collaborators. Before beginning an external lab rotation, students are expected to develop a plan for the scope and expected outcomes of the work to be pursued under the supervision of a named lab rotation advisor. This plan must be pre-approved by the student's academic, research, or thesis advisor in CDS. While desirable, it is neither required nor expected that the rotation will result in completion of a substantial body of work. Lab rotation credits will be noted on the student transcript as a 4-credit pass/fail independent study course numbered as DS-990. -
CDS DS 992: Computing & Data Sciences Research Rotation
Experience with diverse research group projects and styles are an essential part of graduate training. To provide this training, graduate students are expected to complete a series of research rotations that provide them with the opportunity to (1) explore research groups where they may pursue future thesis work; (2) experience the diversity of sub-disciplines and research styles in computing and data sciences; and (3) expand their network of potential research collaborators. Before beginning a rotation, students are expected to discuss their plans with the faculty leadership of the BU research group they would like to join, leading to a clear definition of the scope and expected outcomes of the work pursued under the supervision of a named rotation advisor. While desirable, it is neither required nor expected that the rotation will result in completion of a substantial body of work. Research rotation credits will be noted on the student transcript as a 4-credit independent study course numbered as DS-991 (for Fall semester rotations) or DS-992 (for Spring semester rotations).
