The Capstone course is open to all Harvard students interested in working on a team to integrate the data science and computational science knowledge acquired in core courses to produce an end-to-end solution to a complex real-world problem. The 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.
This course will give you the opportunity to:
1) work as part of a team
2) integrate knowledge acquired in other courses
3) apply skills and ideas in computational and data science
Upon completion of the Capstone Project Course, you will be better prepared to conduct research at a professional level.
A significant component of the course will be regular feedback and guidance from the teaching staff, including explicit measures of evaluation and feedback on all aspects of the project, beginning with data acquisition, through project design and implementation. Additional feedback includes self-assessment and peer evaluation. You will be asked to evaluate each team member’s contribution as well as your own contribution to the project.
Since many employers consider the ability to engage in productive teamwork a requirement for success, we expect you to actively develop your collaborative skills, such as group management, interpersonal communication and conflict resolution.
In the Spring semesters, since the class is drastically smaller, the structure of meetings and progress updates differ from that of the Fall.
In the Spring, most class sessions will be a discussion, whereby each student is expected to give a weekly update. Collectively, we brainstorm and help provide suggestions. During select classes (see the calendar), we will have presentations and guest lectures.
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 Chris; however, the partner should be aware of your work at least once every 2 weeks.
Deliverables and Grades
During the Town Hall meetings, we will spend a brief amount of time on class announcements followed by Lightning Talks or Ignite Talks by some of the groups. You will give two Lighting Talks talks during the semester.
Grade Breakdown [Will be Finalized by Feb 3]
Phase 1 (10 pts)
Statement of work (2 pts)
Ignite Talk (8 pts)
Phase 2 (15 pts)
Initial Partner Report (3 pts)
Milestone #1 Presentation (Midterm) (10 pts) -- March 5
Self and peer evaluation (2 pts) -- March 5
Phase 3 (30 pts)
Partner Report (4 pts) -- March 12
Technical Progress (14 pts) -- March 12
Milestone #2 Presentation (10 pts) -- March 26
Self and peer evaluation (2 pts) -- March 26
Phase 4 (45 pts)
Poster at showcase (8 pts) -- TBD
Technical Progress (10 pts) -- April 9
Partner report (5 pts) -- April 9
Final Presentation (10 pts) -- May 7
Final blog write-up (10 pts) -- May 7
Self and peer evaluation (2 pts) -- May 7
Learning Opportunities for this course
Feedback from the Instructor and Teaching Fellows
The largest component of the course will be the substantial feedback and guidance from the instructor and teaching fellows (TFs), including explicit measures of evaluation, feedback on all aspects of the project and its implementation, as well as self-assessment and peer evaluation. 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 also meet with your project partner organizations a few times but these meetings are for consultation and feedback only. Some partners are more hands-on than others; you and them will have the opportunity to determine the level of partner involvement during the course. As described in the previous paragraph, goals and guidelines are set during meetings with the instructor and TFs and you will be graded by the same people.
Feedback from and Interaction with your teammates
We expect you to work with your team members to develop your collaborative skills such as group management and conflict resolution, and to sustain good interpersonal relations. You will be asked to evaluate each team member’s contribution as well as your own contribution to the project. Many employers consider the ability to engage in productive teamwork a requirement for success.
Learning Outcomes for this course
After you successfully complete this course you will be able to:
Collaborate with others
Acquire, organize and process data (data analysis)
Create and outline solutions using statistics, machine learning and mathematical modeling (data modeling)
Implement those solutions
Communicate and defend your work
Practice professional skills by communicating with partners from outside the University in a courteous and punctual manner.
Change the world!