The project is about solving a business analytics problem proposed by the company. This involves using the analytic skills that the students have learned in their degree program. The type of projects and business domains the previous teams have worked on are as follows:

• Early July (For Semester 1) and Early December (For Semester 2): Deadline for online submission of project description
• After submission: Companies to be informed of selection for project briefing
• Before start of the semester: Companies to submit a recorded presentation about the company and the project
• First instructional week: Students bid for projects based on preference; companies to be informed of bidding results
• By end of first instructional week: Project kickoff; students have visited the company and project supervisors

Go to the Capstone system (https://app.comp.nus.edu.sg/app/capstone/company/) and enter your company details.

Our students are equipped with data management, data visualization, data modeling, and statistical skills, including developing predictive and optimization models using machine learning concepts. In addition, they are competent in R and python.

Students do have access to some tools at NUS like R, python, tableau, which they can use for their work. However, if there is any software required by the company that students do not have access to, then companies will need to provide access.

The other important deliverable from the company would be to provide students with access to the data and resources required to do the project, especially if there are specialized software that the students do not own, or NUS does not provide for them. As the project duration is short, it is important that all data and resources are made available to the students by August.

Supervisors generally meet with students once a week. This is necessary, as the project duration is short, and it would be helpful for students to get feedback weekly to be sure they are on the right track. If multiple groups are assigned to a project, the company can decide whether to meet all the groups in one meeting or separate meetings.

Be the point of contact between students and your company, facilitate and provide access to data and resources required for students to do the project, provide feedback to students to ensure their project is on track and meet business requirements, be available for final presentation and provide feedback/assessment of students’ performance.

It depends. There is no fixed time duration. Based on the students’ questions and feedback from the supervisors, it can be varied. During weekly meetings, supervisors can set milestones, assign tasks, provide technical advice, and any other relevant feedback.

Usually, one team per project, but we may assign a project to multiple groups based on the student enrolment for the semester. At the time of project submission, the company can indicate whether they are willing to work with multiple groups.

The company can decide the number of supervisors to be assigned for each project and they can also decide if the same or different supervisors will supervise different projects.

Usually, there are 5-6 members. This depends on the module enrolment for the semester.

Yes, we’re able to assign the same project to multiple groups. However, please indicate, as shown in the figure below (project submission form in Capstone System), how many project groups you would like to supervise for each project.

Students will bid for the projects based on their preferences, and projects are then assigned by the module instructors.

No, first module instructors will check the project scope, decide whether the scope is suitable for the module, and approve it. Once the project is approved, it is available for students to bid. Then, based on the bidding results, module coordinators will assign the projects to students’ teams.

However, if the student enrolment is low for the semester, not all approved projects will be assigned. The projects that are not assigned can be re-proposed for the next semester via the Capstone system.

Yes, the company will host the students for the kickoff meeting. Thereafter, the students will work mostly remotely since they have to attend classes on campus. If there is a need for them to work on-site due to data security issues, arrangements will have to be made with the students. Some companies have provided remote access for students. It would be good if you could let students know the mode of work in your introduction presentation.

The students are working on this project as part of a module. So, their workload is equivalent to doing two normal modules, which is about a 20-hour workload per week per student. The project’s duration is about 13 weeks, so the project scope and complexity should be around 720-1200 hours of work.

All data and resources required for the projects must be available by the kickoff meeting so the students can start their work right after the kickoff meeting. We do not encourage projects that expect students to collect publicly available data wholly; gathering publicly available data to complement company data is preferred. The students can do some preprocessing to transform the data such that it is useful for the analyses.

No, it is not required.

The students will do a final presentation where the supervisor would be invited to attend. Supervisors will also be given a rating form to provide their feedback and assessment on their performance. Students will also hand over all required deliverables to the company.

Yes, it would not be a problem.

We are unable to share specific examples and companies, as all our projects are bound by NDAs. However, we have done many projects involving various domains, including finance, healthcare, transportation, technology, entertainment, marketing, education, etc. The completed projects have involved developing dashboard applications (e.g., for customer-facing agents/staff, senior management, or analysts), trend analyses from mining and analyzing data (both company data and publicly available data), developing prediction models on demand and sales, and developing and implementing optimization models for resource allocations.