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 MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

  • CDS DS 592: Special Topics in Mathematical and Computational Sciences
    Undergraduate Prerequisites: CASMA242 or equivalent AND CASMA581 or equivalent AND experience writing scientific code
    Spring 2022: Stochastic Processes for the Design and Analysis of Algorithms. Introduction to interplay between stochastic processes and algorithms used in statistics and machine learning. Covers core stochastic processes concepts and use to design and analyze algorithms for sampling and large-scale stochastic optimization. Strong emphasis on practical implications of results.
  • CDS DS 594: Spark! Data Visualization X-Lab Practicum
    Undergraduate Prerequisites: CDS DS 310 ; CDS DS 122 ; CDS DS 121.
    For 2023-2024. The Data Visualization X-Lab Practicum offers students an opportunity to learn data visualization skills through course and project-based work. Projects will be completed on a schedule that aligns with topics being covered in class and assignments. This course provides an accurate experience of solving real-world problems with data visualization, and the various tradeoffs that need to be considered. Whether it's how to efficiently use color and space, effectively understand the profile of a dataset or cautiously avoid bias, this course will provide students with a solid understanding of applicable data visualization practices.
  • CDS DS 595: Special Topics in Physical and Engineering Sciences
    Coverage of a specific topic in relation to physical and engineering sciences in data science. Topics vary semester to semester.
  • CDS DS 596: Special Topics in Natural, Biological and Medical Sciences
    Coverage of a specific topic in relation to natural, biological and medical sciences in data science. Topics vary semester to semester.
  • CDS DS 597: Special Topics in Social and Behavioral Sciences
    Coverage of a specific topic in relation to social and behavioral sciences in data science. Topics vary semester to semester.
  • CDS DS 598: Special Topics in Machine Learning
    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 680: AI Ethics
    This course develops students' ability to critically examine and question the interplay between data science and computational technologies on the one hand, and society and public policy on the other. Students will complete exercises to demonstrate their facility with key ethics tools and techniques, and analyze a series of real-world case studies presented alongside ethical tools and analyses that are useful both for staying alert to emerging ethical challenges and responding to them as they arise in both employment settings and everyday life.
  • CDS DS 682: Responsible AI, Law, Ethics & Society
    Undergraduate Prerequisites: CDSDS100/CDSDS110 (Intro to data science OR equivalent) and CDSDS340 (intro to ML and AI OR equivalent)
    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 701: Tools for Data Science
    This is a new course to be designed specifically for the MS DS program. Students will take this course in their first semester. The goal of the course is to give students exposure to, and practical experience in, formulating data science questions -- particularly learning how to ask good questions in a specific domain. The course will also cover methods of obtaining data and common methods of processing data from a practical standpoint. It will be organized around a semester-long group project in which students are organized into teams and engage with "clients" who bring data science questions from a particular domain. The course will include a formal presentation of results at the end of the semester.
  • 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).