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 199: CDS Workshops (1 credit)
Spring 2022: "Confronting Surveillance: Living In Data Science's Gaze". This guest lecture series examines technologically-enabled surveillance. Topics include history of surveillance, the ramifications of surveillance on individuals and communities, and data's role in identifying oppressive systems. Weekly preparation for the lectures and consistent attendance required. - CDS DS 219: Software Engineering Career Prep Practicum Workshop
Taught by industry software veterans who serve as Spark! Engineers in Residence in CDS, this 2-credit course presents students with an unadulterated view of what they need to know as they take on software engineering projects, in preparation for careers as full-stack software/data engineers. From a brass tacks perspective, the course covers a number of tactical topics. The course covers the language of modern software development including patterns, source control, pull requests, open source, containerization, virtualization, and agile vs waterfall development methods. Additionally, the course introduces students to a few of the specialized professional software engineering and DevOps roles in industry. - CD
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- CDS DS 473: The Justice Media Co-Lab Practicum
This course matches students with interdisciplinary interests in teams that bring expertise in computing to bear on Justice Media problems, in collaboration with faculty and students from the Journalism Department in the College of Communication. The computational journalism projects pursued by these teams are provided by external media partners and are curated by Spark! These projects vary in size and scope and range from smaller projects that are approximately 60 hours of work to larger projects that can be as much as 100 hours of work per team member over the course of a semester. Projects involve teams of approximately 3 students from computing disciplines, and require partner organizations to dedicate approximately 45 minutes per week to work with the students, for the 8-12 weeks of project implementation. - CDS DS 482: Responsible AI, Law, Ethics & Society
Course page: https://learn.responsibly.ai/. Instructor: shlomi@bu.edu. This course addresses the deployment of Artificial Intelligence systems in multiple domains of society, and how this raises fundamental challenges and concerns, such as accountability, liability, fairness, transparency and privacy. 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 unique course will bring together students from either computing and data science disciplines or law and public policy disciplines from multiple institution. Effective Spring 2022, this course fulfills a single unit in each of the following BU Hub areas: Ethical Reasoning, Social Inquiry II, Teamwork/Collaboration. - CDS DS 499: CDS Practicum Course
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
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. - CDS DS 537: Data Science for Conservation Decisions
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
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 549: Spark! Machine Learning X-Lab Practicum
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. - CDS DS 563: Algorithmic Techniques for Taming Big Data
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. Effective Fall 2021, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation. - CDS DS 571: Tools Data Sci
- 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. - 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 649: Advanced Topics in DS
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