Data Science

The listing of a course description here does not guarantee a course’s being offered in a particular term. 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 596: Special Topics in Natural, Biological and Medical Sciences
  Spring 2027 Subject: AI in Weather & Climate Simulation: From Days-to-Decades Deep-learning models can now produce global weather forecasts in seconds that rival those from traditional numerical models that have taken decades to develop. And they're getting better fast. At the same time, researchers are pushing these models beyond weather to simulate the earth system on timescales of seasons, years, and decades. You'll work hands-on with state-of-the-art AI weather and climate models, learning how they are designed and trained, as well as how to run them, evaluate them, and diagnose where they succeed and fail. Topics include working with large spatio-temporal datasets, fundamentals of data-driven time series simulation, probabilistic prediction, forecast verification and evaluation metrics, choices in deep-learning architecture and training (generative approaches, autoregressive rollout, embedding physical constraints), and current open challenges: blurring, checkpoint selection, chaos, and the problem of climate change itself pushing models out-of-distribution.
• 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
  Enrollment Requirements: Undergraduate Prerequisite: Junior Standing or higher; and (CDSDS 110 and CDSDS 121 and CDSDS 320) Graduate Prerequisite: Graduate Standing
  Spring 2027 Topic - Graphs in Machine Learning Complex data in many real-world systems, such as social, technological, and biological networks, can often be represented as graphs, where objects are nodes and their relationships are edges. This course introduces key concepts and methods for machine learning with graph-structured data. We begin with foundational ideas in graph theory and classical measures of importance in networks, then explore topics such as graph mining, spectral methods, and graph embeddings. The second half of the course focuses on Graph Neural Networks (GNNs), covering their core principles, representative architectures, and applications to node- and graph-level classification.
• 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 630: Introduction to Bioinformatics and Computational Biology
  Bioinformatics is an interdisciplinary filed combing data science, computing, algorithms, and programming with biology. This course teaches the fundamental algorithms that form the backbone of modern bioinformatics as well as their implementations and applications to data. Topics covered include genome assembly, sequence alignment, phylogenetic trees, gene regulation, and large-scale genomics data as well as associated computational methods including graph algorithms, dynamic programming, combinatorial pattern matching, tree algorithms, and machine learning.
• CDS DS 653: Crypto for Data Science
  This course investigates techniques for performing trustworthy data analyses without a trusted party, and for conducting data science without data. The first half of the course investigates cryptocurrencies, the blockchain technology underpinning them, and the incentives for each participant, while the second half of the course focuses on privacy and anonymity using advanced tools from cryptography. The course concludes with a broader exploration into the power of conducting data science without being able to see the underlying data.
• 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: Data, Society, and AI Ethics
  Enrollment Requirements: Graduate Prerequisite: Graduate Standing
  This course develops students' ability to critically examine and question the interplay between artificial intelligence (AI), 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. Effective Spring 2026, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Research and Information Literacy, Social Inquiry 2.
  BU Hub Learn More

  • Ethical Reasoning
  • Research and Information Literacy
  • Social Inquiry II
• CDS DS 682: Responsible AI, Law, Ethics & Society
  Enrollment Requirements: Undergraduate Prerequisite: (CDSDS 100 or CDSDS 110) and CDSDS 340 - Grade of C or higher required in all courses
  This course addresses the deployment of Artificial Intelligence systems across various societal domains, raising fundamental challenges and concerns such as accountability, liability, fairness, transparency, and privacy. Tackling these challenges necessitates an interdisciplinary approach, integrating principles and practices from data science, ethics, and law. This unique course will bring together students from computing and data science disciplines as well as law and public policy disciplines from multiple institutions. Permission is required to register for this course. Course page: https://learn.responsibly.ai. Please fill out an application form here: https://forms.gle/bMRECdYcMUwHj7xG8. Instructor: shlomi@bu.edu.
• 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 701: Tools for Data Science
  This course is a Master's level introduction to data science, focusing on proficiency in working with and analyzing data. The course emphasizes practical skills in working with data, while introducing students to a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. The goal of the class is to provide to students a hands-on understanding of classical data analysis techniques and to develop proficiency in applying these techniques in a modern programming language (Python).
• CDS DS 719: Data Science Product Management 1
  DS PROD MGNT 1
• CDS DS 729: Data Science Product Management 2
  DS PROD MGNT 2
• CDS DS 790: MSDS Internship
  Internship course for MSDS students only.
• CDS DS 791: Teaching Practicum 1
  This is the first of two required 2-credit courses that must be taken before the last semester in which a student is enrolled in the PhD program in CDS. These two courses (DS-791 and DS-792) are designed to provide graduate students with the practical training necessary to be effective teachers not only in classroom settings but also in research settings that require the communication of technical concepts to peers and mentees. In DS-791, students will be assigned as teaching fellows working as apprentices under a faculty member in support of various teaching duties. Depending on the nature of the teaching assignment, this may include leading discussion sections or labs, offering office hours to assist learners with homework assignments, engaging with learners through online learning management systems, helping with learning assessments, etc. In addition, students are expected to participate in regular meeting of the two courses (DS-791 and DS-792) that introduce them to pedagogical innovation and best practices in communicating and teaching a variety of data science subjects.
• CDS DS 792: Teaching Practicum 2
  Enrollment Requirements: Graduate Prerequisite: CDSDS 791
  This is the second of two required 2-credit courses that must be taken before the last semester in which a student is enrolled in the PhD program in CDS. These two courses are designed to provide graduate students with the practical training necessary to be effective teachers not only in classroom settings but also in research settings that require the communication of technical concepts to peers and mentees. In this course, students will be assigned as teaching fellows working as apprentices under a faculty member. Beyond the teaching duties in DS-791, students taking this course are expected to take on supervisory roles involving teaching assistants, class assistants, or graders. In addition, students are expected to participate in regular meeting of the two courses (DS-791 and DS-792) that introduce them to pedagogical innovation and best practices in communicating and teaching a variety of data science subjects.
• CDS DS 795: Urban Biogeoscience and Environmental Health: From Research to Policy
  Students learn how cities utilize scientific findings to address urban environmental challenges and develop communication skills to effectively translate scientific results to decision-makers and the public. Students complete a semester-long internship to gain practical experience as a required component of this course.
• CDS DS 799: Master Thesis
  Master Thesis for MSDS.
• CDS DS 900: Graduate Internship in Computing & Data Sciences
  For graduate students in Computing & Data Science, this internship course gives students substantive practical experience in industry. This course may be taken with approval from the PhD program. Bi-weekly and final reports required.
• 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.