Previous Capstone Syllabi

Your faculty will share more information about Capstone in the second semester of your senior year. If you’d like to get a taste for what the Capstone project is like, read some examples of syllabi from years past.

  • Team T Capstone Syllabus
  • Team Y Capstone Syllabus
  • Capstone Syllabus for all other teams
  • Team R Capstone Syllabus
  • Team W Capstone Syllabus
  • 2019: Team S Capstone Syllabus

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 Wednesdays @ 12:45pm - 3:00pm SEC LL2.223 (Allston Campus)

capstone project syllabus

Overview The Capstone course is open to students interested in working on a team to integrate data science and computational science knowledge to produce an end-to-end solution to a complex real-world problem. Projects are selected so that current statistical, machine learning, computational, and engineering methods can be used. Most projects have been designed to address important contemporary business and societal issues.   During the semester, students will work in teams of 4-5 on a single project for one Capstone partner organization. This experience will help students build skills in public speaking, technical reading/writing, management of team dynamics, and examination of their work under the lenses of diversity and inclusion.

Learning outcomes, in this course you will: , develop skills to manage the dynamics of a diverse team (both peers and supervisors), develop skills to communicate with and balance the interests of multiple stakeholders (both technical and non-technical) on a project, synthesize and apply technical knowledge acquired in other courses to real-life problems, develop a fundamental understanding of all aspects of the data science/machine learning pipeline, including in what ways each part of the pipeline can significantly affect the others., think broadly and critically about the implications of technical design choices: from data collection to assessment of the downstream socio-technical impact, upon completion of the capstone course, you will be better prepared for data science and machine learning work in a professional setting (both academic and industry)..



Capstone students will work in small teams on projects provided by capstone partner organizations. in addition to technical merit, students will be evaluated on how well they manage their relationships with different stakeholders throughout the semester. many employers consider the ability to engage in productive teamwork a requirement for success. thus, in this class, we expect you to actively build your collaborative skills, such as team management, interpersonal communication, and conflict resolution. in fact, a significant component of the course will consist of students soliciting and incorporating feedback from multiple diverse perspectives (from the teaching staff, the partners, project mentors, team members, and other teams). , students will be responsible for two types of meetings., class meetings, the class will meet once weekly. a typical class session will begin with a guest lecture (led by an expert on a topic at the intersection of ai and broader social issues), or a project-related hands-on group activity. , meetings with mentors and partners, each group will meet with its project mentor on a weekly basis. these meetings can take place during class (if there is no scheduled guest lecture or other activities). students are expected to maintain and structure communication with their project mentors and instructors in ways that best suit their needs and everyone's schedules., each group will meet with their respective partner on a regular basis to provide updates. the exact frequency will be collectively decided upon by the partner, your group, and your instructor; however, the partner should be aware of your work through some means of communication at least once every 2 weeks. .


Deliverables and Grades


Early in the semester, during class time, each group will deliver a 4-minute lightning talk (aka ignite talk). this is where each team sets the stage for their project while outlining and motivating the problem., teams will additionally deliver 2 milestone presentations and 1 final presentation to the entire class. , phase 1 (10 pts), ignite talk (5 pts), problem statement (5 pts), phase 2 (15 pts), milestone #1 presentation (6 pts), partner progress report (6 pts), self-/peer- evaluation (2 pts), feedback for other groups' reports (2 pt), phase 3 (18 pts), milestone #2 presentation (7 pts)  , partner progress report (7 pts), phase 4 (47 pts)​, final presentation to class (10 pts), partner progress report (10 pts), final write-up blog post (10 pts), poster (5 pts), self-/peer- evaluation ( 2 pts), code base (runs, is organized, and readable) (10 pts), class participation (9 pts), 1. guest lecture summary reflections (1 pt each), extra credit (3 pts per award), gsd award (best design), winston churchill award (best oral presentation), graduate advisory committee award (best analysis of broader impact), learning opportunities for this course, feedback from the instructor and teaching fellows, students will receive feedback and guidance from the instructor and teaching fellows (tfs), including explicit measures of evaluation, and feedback on all aspects of the project and its implementation. this feedback process will begin with the data acquisition and data exploration phase and extend through the design and implementation phase of the project., feedback from the project partner organizations, you will meet with your project partner organizations throughout the semester for consultation and feedback. you and your partner organization will have the opportunity to determine the level of partner involvement during the course. , feedback from teammates, we expect you to maintain a productive and inclusive working dynamic within your team. towards this end, you will be asked to evaluate each team member’s contribution as well as your own contribution to the project. , feedback from other teams, for each major deliverable, you will provide and receive feedback from other capstone teams. peer feedback will be guided by concrete evaluation rubrics..


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