Courses
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.
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CDS DS 381: Social Justice for Data Science
Society is becoming increasingly digitized and datafied. Important decisions impacting criminal justice, housing, finance, labor, healthcare, and education are frequently determined by or are aided by artificially intelligent algorithmic technologies that are built and trained on large datasets. The rise in these technologies presents a challenge for social justice. Though often presented as neutral decision aids, these technologies often produce harmful predictions that operate to reinforce old legacies of racial, class, gender, and heteropatriarchal subordination. Datafication practices, computational techniques, legal doctrine, and policy play a key role in facilitating these disparate outcomes. This course will center on the complicated relationship between social justice and data science. The course will introduce students to the historical and current role of datafication and computation practices in social subordination. Students will leave the course having developed the skill set needed to identify and critically engage with the social justice challenges posed by these new technologies. -
CDS DS 453: Crypto for Data Science
Undergraduate Prerequisites: DS-122 and DS-320, or equivalent.
CDS DS 453 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 457: 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 481: Spark! Data Science for Good: Topics in Civic Tech
This course enables students to tackle real world data challenges related to a more equitable and just society. Students will work in teams on projects addressing pressing societal challenges in the public sphere, provided by partners from the public sector. Course emphasizes teamwork, client/project management, data collection/engineering, analytics and/or software development, testing and delivery of technical artifacts, and research and presentation of final deliverables -
CDS DS 482: Responsible AI, Law, Ethics & Society
Undergraduate Prerequisites: CDSDS100/CDSDS110 (Intro to data science OR equivalent) and CDSDS340 (intro to ML and AI OR equivalent)
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. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Social Inquiry II, Ethical Reasoning, Teamwork/Collaboration -
CDS DS 488: Spark! UX Design X-Lab Practicum
Undergraduate Prerequisites: CDSDS280 OR equivalent
This course gives students an opportunity to apply methods and practices of user experience design to real-world projects. Students work in teams to address needs of industry partners for applying interactive software to solve practical problems. Addresses all phases of the user experience design process from user research and discovery to design and validation, with a focus on mastering techniques and methods for learning about users, applying design thinking methods to conceive and iterate on solutions, and validating designs through user testing and feedback. -
CDS DS 490: 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 499: CDS Practicum Course
Undergraduate Prerequisites: consent of instructor
Courses engage students in interdisciplinary computing and data science projects. Projects may support CDS co-Labs, in partnership with internal and external organizations. Opportunities to connect computing and data sciences with domain-specific knowledge and expertise to advance co-Lab priorities. -
CDS DS 519: Spark! Software Engineering X-Lab Practicum
Undergraduate Prerequisites: CDSDS310 OR CASCS411 OR equivalent experience in software developmentand consent of instructor. Consent provided upon successful completion of pass/fail diagnostic test to assess student readiness fo
This course offers students in computing disciplines the opportunity to apply their programming and system development skills by working on real-world projects provided from partnering organizations within and outside of BU, which are curated by Spark! The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include teamwork and communications skills and software development processes. All students participating in the course are expected to complete a software engineering project including a final presentation to the partner organization. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Oral and/or Signed Communication, Teamwork/Collaboration. -
CDS DS 522: Stochastic Methods for Algorithms
Undergraduate Prerequisites: First-Year Writing Seminar (e.g., WR 120); CAS CS111, CDS DS110, ENG EK125, or equivalent; CAS MA225, CAS CS235, CDS DS122, or equivalent; CAS MA242, CAS CS132, CDS DS121, or equivalent; CAS MA581, CA
Graduate Prerequisites: CAS CS111, CDS DS110, ENG EK125, or equivalent; CAS MA225, CAS CS235,CDS DS122, or equivalent; CAS MA242, CAS CS132, CDS DS121, or equivalent; CAS MA581, CAS CS237, ENG EK381, ENG EK500, or equivalen
Application of stochastic process theory to design and analyze algorithms used in statistics and machine learning, especially Markov chain Monte Carlo and stochastic optimization methods. Emphasizes connecting theoretical results to practice through combination of proofs, numerical experiments, and expository writing. Effective Fall 2023, this course fulfills a single unit in each of the following BU Hub areas: Writing-Intensive Course, Creativity/Innovation. -
CDS DS 526: Critical Reading in Biological Data Science
The goal of this course is to provide students with a framework, skills, and knowledge to critically evaluate research in biological data science. Biological research is rarely unequivocal in its findings; students will learn to systematically identify the claims advanced in research papers and evaluate whether each claim is established beyond a reasonable doubt by supporting evidence. We will examine papers that both meet and fail this test. In today's biology, to properly examine a paper in this way it is increasingly important to engage with the data provided as supporting evidence, and to critically examine the computational approach. Students will work with published data and computational tools. Further, students will learn to identify the ideology implicit in each paper, to understand how ideology shapes both the research questions and approach, and to imagine the same research under an alternative mindset. Classes will be split into lectures on background material for each paper and group discussions. Students will work in small groups to write a report on each paper. Each student will work on a final project to produce a critical review of a broader topic in the field. Pre-Reqs: CDSDS 120, 121, and 122 or equivalent; ENGBE 562 or equivalent or experience with computational biology -
CDS DS 537: Data Science for Conservation Decisions
Undergraduate Prerequisites: CASGE/EE 270 or equivalent; GE/EE 375 or equivalent; or consent of instructor.
This course covers the application of quantitative methods to support conservation decisions. Ecosystem value mapping, systematic conservation planning, policy instrument design, rigorous impact evaluation, decision theory, data visualization. Implementations in state-of-the-art open-source software. Real-life case studies from the U.S. and abroad. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Digital/Multimedia Expression, Quantitative Reasoning II, Research and Information Literacy. -
CDS DS 539: Spark! Data Science X-Lab Practicum
Undergraduate Prerequisites: CASCS506 or equivalent preferred. CDSDSDS110 OR CASCS111 OR CASCS112 OR equivalent. CDSDS121 OR CASCS132 OR equivalent required. Or instructor consent which may involve pass/fail diagnostic test.
This course offers students in computing disciplines the opportunity to apply their data science skills by working on real-world projects provided from partnering organizations within and outside of BU, which are curated by Spark! The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include communications skills, collaborative work processes and an assessment of the ethical considerations of their work. All students participating in the course are expected to complete a data science project including a final presentation to the partner organization. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Digital/Multimedia Expression, Research and Information Literacy, Teamwork/Collaboration. -
CDS DS 541: Agent-Based Modeling of People, Health, and the Environment
Agent-based models are an ideal tool for analyzing systems (cities, economies, road networks, forests) in which the outcomes of interest (illness rates, traffic problems, land use, violence) are shaped strongly by interactions among individual 'agents' (drivers, consumers, farmers, spouses) with each other and their environments. This course builds skills in the use of agent-based modeling using the NetLogo platform as a tool for analyzing complex human- environment problems. The course will emphasize the iterative model-building process -- forming research questions, building conceptual and then operational models, experimenting, and then refining -- to address current research problems. Students should expect to commit time outside of meetings to i) gaining comfort with NetLogo syntax; ii) reading current research in the areas of agent-based modeling and human environment and health problems; and iii) a team based approach to building models. -
CDS DS 549: Spark! Machine Learning X-Lab Practicum
Undergraduate Prerequisites: CDSDS340 OR CASCS542 OR CASCS505 OR CASCS585 OR consent of instructor. Consent may include the successful completion of a pass/fail diagnostic test that will assess student readiness to take the cours
The Spark! Practicum offers students in computing disciplines the opportunity to apply their knowledge in algorithms, inferential analytics, and software development by working on real-world projects provided from partnering organizations within BU and from outside. The course offers a range of project options where students can improve their technical skills, while also gaining the soft skills necessary to deliver projects aligned to the partner's goals. These include teamwork and communications skills and software development processes. All students participating in the course are expected to complete a project focused on an application of inferential analytics or machine learning, including a final presentation to the partner organization. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Research and Information Literacy, Teamwork/Collaboration. -
CDS DS 561: Software Engineering Development on Modern Cloud Environments
Most of today's organizations needing a technology solution look to satisfy their computing, storage and networking needs through one of the large public cloud providers. Unlike traditional environments where a company had to build its own infrastructure often at large time and monetary expense it can now rent what it needs at the click of a button. In this course we will provide hands on experience with one of the large public cloud platforms. In particular we will look into the different flavors of compute, storage and networking available, how best to use them to solve interesting problems, and how to do everything on a constrained budget. Students will get accounts and deliver project work on the public cloud while also learning some of the fundamental principles on how those different cloud systems work under the covers. It is recommended that students taking this class have learned the basic principles of Computer Systems such as those taught in DS210 and/or CS210. -
CDS DS 563: Algorithmic Techniques for Taming Big Data
Undergraduate Prerequisites: CDSDS110 OR CASCS111 OR ENGEK125 OR equivalent; CDSDS320 OR CASCS330 OR ENGEC330 OR equivalent; CDSDS121 OR CASCS132 OR CASMA242 OR equivalent; CASMA115 OR CASCS327 OR ENGEK381 OR equivalent, OR conse
Growing amounts of available data lead to significant challenges in processing them efficiently. In many cases, it is no longer possible to design feasible algorithms that can freely access the entire data set. Instead of that we often have to resort to techniques that allow for reducing the amount of data such as sampling, sketching, dimensionality reduction, and core sets. Apart from these approaches, the course will also explore scenarios in which large data sets are distributed across several machines or even geographical locations and the goal is to design efficient communication protocols or MapReduce algorithms. The course will include a final project and programming assignments in which we will explore the performance of our techniques when applied to publicly available data sets. -
CDS DS 574: Algorithmic Mechanism Design
Undergraduate Prerequisites: CDSDS122, CDSDS320, and CASMA581 or instructor approval
This course is an introduction to the interdisciplinary area of Algorithmic Mechanism Design: where computational perspectives are applied to economic problems, and economic techniques are brought to problems from computer science. We will explore a broad range of topics at the frontier of new research, starting with some of the fundamentals, such as welfare-maximizing auctions and types of Nash Equilibria. Throughout the semester, the class will also learn about prevalent topics such as (1) Data Science & Incentives, (2) Mechanism Design for Social Good, and (3) optimization and robustness in mechanism design. -
CDS DS 587: Data Science in Human Contexts
Where do statistical and computational insights lose historic social contexts? What are the impacts of datafication on individuals and communities? How do social and technical systems reify or challenge social hierarchies? Through a survey of academic literature, community-produced knowledge and coverage of technology in the popular press, this course will explore these themes as they relate to labor and automation, surveillance and the legal system, social media governance, and digital inclusion. -
CDS DS 590: CDS Research Initiation 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.
