DATA 730 – Fieldwork Experience – MS in Data Science
Sponsored Fieldwork Experience Projects allow our students to work on real-world projects, in collaboration with industry sponsors and RCNJ faculty. Each Fieldwork project is assigned a faculty mentor, and one or more students. Students register for Fieldwork as part of their regular graded coursework, so their efforts on your project come at no cost to your organization.
To participate in the Fieldwork Experience program, you must submit an application by the deadline for the upcoming cycle (see below) and pay an administrative fee.
Your level of participation in the project is up to you. You may choose to meet regularly with the students working on the project, and take the lead in guiding them. Alternatively, you may set general goals and allow the faculty mentor to guide students towards project completion.
Should your project need to leverage the expertise of our faculty from both within and outside the Data Science department, we can bring those faculty members into the project to work directly as team members.
A Fieldwork project can be focused on any topic or goal important to your organization. The typical project may include data collection, cleaning, analysis, and presentation – aimed at delivering some real, measurable value to your organization. Our students will be prepared to leverage skills in computer programming, statistics, machine learning, and data visualization. In addition, all students participating in Fieldwork have completed our Data Ethics course, and have been trained to handle data responsibly.
We recognize the need and value of Data Scientists bringing domain knowledge to their work as well. We encourage our students to develop expertise in areas such as healthcare, business, social and political sciences, and more – and provide opportunities for them to take coursework in these areas. We will work with you to identify the type of domain experience valuable to your project, and match you with students that share those interests whenever possible.
Organizations must submit an application to the MSDS program by the deadlines listed below. The application should contain the following (preliminary) information:
- A one to two page summary of the project requirements and goals. In particular, we’d like to hear what your expectations of the project outcomes are, why it’s important to your organization, and why you think it would be a good fit for a student project.
- Is the data used in the project sensitive in nature, and are there any policies or applicable law that influences the way the data can be handled?
- Do you plan to have employees at your organization participate in the project with students, or do you prefer to have the students (and / or RCNJ faculty) take the lead?
- Is any of the work required to be completed on-site, or on your organization’s computing and data platforms?
||Once we have your application we’ll begin working with you to understand your project needs.
|Feasibility Analysis and Decision
||Program Director and Sponsor agree on a general project plan, with specifics to be further elaborated during the Planning Phase. Administrative Fee is due for all accepted projects.
|Planning Phase Begins
||The project is scheduled to be offered in the next semester, a faculty mentor is designated, and detailed planning on project scope begins. Students are selected to work on the project.
|Project Implementation Begins
||Students meet regularly with their faculty mentor and your organization, providing frequent updates on their progress.
||At the end of each semester, our students present their Fieldwork results at our Data Science Fair.
Students must register for Fieldwork on a semester to semester basis, but can take the Fieldwork course more than once. We encourage long term projects, where multiple teams of students may work on different phases of the same project for multiple semesters or academic years.
We will work to maximize continuity between semesters, preferentially placing students on the same project (if desired) and assigning the same faculty mentors. There is no application necessary for continued projects. Projects spanning multiple semesters do require payment of half of the administrative fee, which covers the cost of faculty mentors.
Faculty Team Members
Fieldwork project scope may exceed the capabilities of a single student, or a team of students. This may be due to the volume of work necessary, or because the work requires specific expertise beyond the capabilities of an MSDS student. Sponsors may (and encouraged to) include their own personnel in a project to augment the team’s capacity or expertise.
Sponsors who do not have internal resources to fulfill the additional project needs may also engage RCNJ faculty with required expertise. We will work with you to identify the right faculty member(s) for your project – from within the Data Science faculty or from other faculty groups across the College.
Faculty Team Member fees are determined by a tiered pricing model based on necessary time commitment, estimated during the planning phase of the project.
Computing and Data Infrastructure
If a project requires specialized computing resources / data storage that a project sponsor does not wish to provide, RCNJ computing infrastructure may be used. In most cases, the MSDS program will utilize Amazon Web Services (AWS) to fulfill these infrastructure needs, however we can work with you to identify the most appropriate resources for your project. RCNJ will bill sponsors on a monthly basis for services used during the project.
Fieldwork projects often expose students to proprietary company information. Ramapo College, along with the students and faculty often sign separate non-disclosure agreements (NDA). Even with an executed NDA, company executives may permit students to speak in general terms about their assignment and, in some cases, list the research on their resumes. Regardless of whether an NDA is required, students are expected to treat the assignment as a private matter and conduct themselves in a professional manner at
RCNJ Data Science Contacts